{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objective\n",
    "* 20190102: \n",
    "    * Predict stock price in next day using long short term memory(LSTM)\n",
    "    * Given prices for the last N days, we do prediction for day N+1\n",
    "    * Here we split 3 years of data into train(60%), dev(20%) and test(20%)\n",
    "    * Here we use MinMaxScalar to fit_transform the train set, which we then use to transform the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import math\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "from datetime import date\n",
    "from matplotlib import pyplot as plt\n",
    "from numpy.random import seed\n",
    "from pylab import rcParams\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from tqdm import tqdm_notebook\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow import set_random_seed\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.utils import plot_model\n",
    "\n",
    "#### Input params ##################\n",
    "stk_path = \"./data/VTI.csv\"\n",
    "test_size = 0.2                # proportion of dataset to be used as test set\n",
    "cv_size = 0.2                  # proportion of dataset to be used as cross-validation set\n",
    "\n",
    "N = 9                          # for feature at day t, we use lags from t-1, t-2, ..., t-N as features. \n",
    "                               # initial value before tuning\n",
    "lstm_units=50                  # lstm param. initial value before tuning.\n",
    "dropout_prob=1                 # lstm param. initial value before tuning.\n",
    "optimizer='adam'               # lstm param. initial value before tuning.\n",
    "epochs=1                       # lstm param. initial value before tuning.\n",
    "batch_size=1                   # lstm param. initial value before tuning.\n",
    "\n",
    "model_seed = 100\n",
    "\n",
    "fontsize = 14\n",
    "ticklabelsize = 14\n",
    "####################################\n",
    "\n",
    "# Set seeds to ensure same output results\n",
    "seed(101)\n",
    "set_random_seed(model_seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_mape(y_true, y_pred): \n",
    "    \"\"\"\n",
    "    Compute mean absolute percentage error (MAPE)\n",
    "    \"\"\"\n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "def get_x_y(data, N, offset):\n",
    "    \"\"\"\n",
    "    Split data into x (features) and y (target)\n",
    "    \"\"\"\n",
    "    x, y = [], []\n",
    "    for i in range(offset, len(data)):\n",
    "        x.append(data[i-N:i])\n",
    "        y.append(data[i])\n",
    "    x = np.array(x)\n",
    "    y = np.array(y)\n",
    "    \n",
    "    return x, y\n",
    "\n",
    "def train_pred_eval_model(x_train_scaled, \\\n",
    "                          y_train_scaled, \\\n",
    "                          x_cv_scaled, \\\n",
    "                          y_cv_scaled, \\\n",
    "                          scaler, \\\n",
    "                          lstm_units=50, \\\n",
    "                          dropout_prob=0.5, \\\n",
    "                          optimizer='adam', \\\n",
    "                          epochs=1, \\\n",
    "                          batch_size=1):\n",
    "    '''\n",
    "    Train model, do prediction, scale back to original range and do evaluation\n",
    "    Use LSTM here.\n",
    "    Returns rmse, mape and predicted values\n",
    "    Inputs\n",
    "        x_train_scaled  : e.g. x_train_scaled.shape=(451, 9, 1). Here we are using the past 9 values to predict the next value\n",
    "        y_train_scaled  : e.g. y_train_scaled.shape=(451, 1)\n",
    "        x_cv_scaled     : use this to do predictions \n",
    "        y_cv_scaled     : actual value of the predictions (scaled)\n",
    "        scaler          : scaler that is used to fit_transform train set\n",
    "        lstm_units      : lstm param\n",
    "        dropout_prob    : lstm param\n",
    "        optimizer       : lstm param\n",
    "        epochs          : lstm param\n",
    "        batch_size      : lstm param\n",
    "    Outputs\n",
    "        rmse            : root mean square error\n",
    "        mape            : mean absolute percentage error\n",
    "        est             : predictions\n",
    "    '''\n",
    "    # Create the LSTM network\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=lstm_units, return_sequences=True, input_shape=(x_train_scaled.shape[1],1)))\n",
    "    model.add(Dropout(dropout_prob)) # Add dropout with a probability of 0.5\n",
    "    model.add(LSTM(units=lstm_units))\n",
    "    model.add(Dropout(dropout_prob)) # Add dropout with a probability of 0.5\n",
    "    model.add(Dense(1))\n",
    "\n",
    "    # Compile and fit the LSTM network\n",
    "    model.compile(loss='mean_squared_error', optimizer=optimizer)\n",
    "    model.fit(x_train_scaled, y_train_scaled, epochs=epochs, batch_size=batch_size, verbose=0)\n",
    "    \n",
    "    # Do prediction\n",
    "    est_scaled = model.predict(x_cv_scaled)\n",
    "    est = scaler.inverse_transform(est_scaled)\n",
    "    \n",
    "    # Get correct scale of y_cv\n",
    "    y_cv = scaler.inverse_transform(y_cv_scaled)\n",
    "\n",
    "    # Calculate RMSE and MAPE\n",
    "    rmse = math.sqrt(mean_squared_error(y_cv, est))\n",
    "    mape = get_mape(y_cv, est)\n",
    "    \n",
    "    return rmse, mape, est"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>107.510002</td>\n",
       "      <td>107.660004</td>\n",
       "      <td>107.250000</td>\n",
       "      <td>107.470001</td>\n",
       "      <td>101.497200</td>\n",
       "      <td>1820300</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-27</td>\n",
       "      <td>107.589996</td>\n",
       "      <td>107.760002</td>\n",
       "      <td>107.220001</td>\n",
       "      <td>107.629997</td>\n",
       "      <td>101.648300</td>\n",
       "      <td>552400</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>107.779999</td>\n",
       "      <td>107.849998</td>\n",
       "      <td>107.110001</td>\n",
       "      <td>107.169998</td>\n",
       "      <td>101.213867</td>\n",
       "      <td>3618100</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>107.589996</td>\n",
       "      <td>108.209999</td>\n",
       "      <td>107.370003</td>\n",
       "      <td>108.180000</td>\n",
       "      <td>102.167740</td>\n",
       "      <td>2443600</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>108.099998</td>\n",
       "      <td>108.269997</td>\n",
       "      <td>106.879997</td>\n",
       "      <td>107.050003</td>\n",
       "      <td>101.100533</td>\n",
       "      <td>2937200</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date        open        high         low       close   adj_close  \\\n",
       "0 2015-11-25  107.510002  107.660004  107.250000  107.470001  101.497200   \n",
       "1 2015-11-27  107.589996  107.760002  107.220001  107.629997  101.648300   \n",
       "2 2015-11-30  107.779999  107.849998  107.110001  107.169998  101.213867   \n",
       "3 2015-12-01  107.589996  108.209999  107.370003  108.180000  102.167740   \n",
       "4 2015-12-02  108.099998  108.269997  106.879997  107.050003  101.100533   \n",
       "\n",
       "    volume  month  \n",
       "0  1820300     11  \n",
       "1   552400     11  \n",
       "2  3618100     11  \n",
       "3  2443600     12  \n",
       "4  2937200     12  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(stk_path, sep = \",\")\n",
    "\n",
    "# Convert Date column to datetime\n",
    "df.loc[:, 'Date'] = pd.to_datetime(df['Date'],format='%Y-%m-%d')\n",
    "\n",
    "# Change all column headings to be lower case, and remove spacing\n",
    "df.columns = [str(x).lower().replace(' ', '_') for x in df.columns]\n",
    "\n",
    "# Get month of each sample\n",
    "df['month'] = df['date'].dt.month\n",
    "\n",
    "# Sort by datetime\n",
    "df.sort_values(by='date', inplace=True, ascending=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'USD')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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PwB9/AEOGHPHquUYP2eWXA5s2ybFjz1l12rSp/pqnc87WrZN9PKdMAcaMAT79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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "ax = df.plot(x='date', y='adj_close', style='b-', grid=True)\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split into train, dev and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use lags up to N number of days to use as features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_train = 453\n",
      "num_cv = 151\n",
      "num_test = 151\n",
      "train.shape = (453, 2)\n",
      "cv.shape = (151, 2)\n",
      "train_cv.shape = (604, 2)\n",
      "test.shape = (151, 2)\n"
     ]
    }
   ],
   "source": [
    "# Get sizes of each of the datasets\n",
    "num_cv = int(cv_size*len(df))\n",
    "num_test = int(test_size*len(df))\n",
    "num_train = len(df) - num_cv - num_test\n",
    "print(\"num_train = \" + str(num_train))\n",
    "print(\"num_cv = \" + str(num_cv))\n",
    "print(\"num_test = \" + str(num_test))\n",
    "\n",
    "# Split into train, cv, and test\n",
    "train = df[:num_train][['date', 'adj_close']]\n",
    "cv = df[num_train:num_train+num_cv][['date', 'adj_close']]\n",
    "train_cv = df[:num_train+num_cv][['date', 'adj_close']]\n",
    "test = df[num_train+num_cv:][['date', 'adj_close']]\n",
    "\n",
    "print(\"train.shape = \" + str(train.shape))\n",
    "print(\"cv.shape = \" + str(cv.shape))\n",
    "print(\"train_cv.shape = \" + str(train_cv.shape))\n",
    "print(\"test.shape = \" + str(test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaler.data_min_ = [87.91394]\n",
      "scaler.data_max_ = [125.576111]\n",
      "x_train.shape = (444, 9, 1)\n",
      "y_train.shape = (444, 1)\n"
     ]
    }
   ],
   "source": [
    "# Converting dataset into x_train and y_train\n",
    "# Here we only scale the train dataset, and not the entire dataset to prevent information leak\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "train_scaled = scaler.fit_transform(np.array(train['adj_close']).reshape(-1,1))\n",
    "print(\"scaler.data_min_ = \" + str(scaler.data_min_))\n",
    "print(\"scaler.data_max_ = \" + str(scaler.data_max_))\n",
    "\n",
    "# Split into x and y\n",
    "x_train, y_train = get_x_y(train_scaled, N, N)\n",
    "print(\"x_train.shape = \" + str(x_train.shape)) # (446, 7, 1)\n",
    "print(\"y_train.shape = \" + str(y_train.shape)) # (446, 1)\n",
    "\n",
    "# x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)) # (446, 7, 1)\n",
    "# print(\"x_train.shape = \" + str(x_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_cv.shape = (151, 9, 1)\n",
      "y_cv.shape = (151, 1)\n"
     ]
    }
   ],
   "source": [
    "# Scale the cv dataset according the min and max obtained from train set\n",
    "train_cv_scaled  = scaler.transform(np.array(train_cv['adj_close']).reshape(-1,1))\n",
    "\n",
    "# Split into x and y\n",
    "x_cv, y_cv = get_x_y(train_cv_scaled, N, num_train)\n",
    "print(\"x_cv.shape = \" + str(x_cv.shape))\n",
    "print(\"y_cv.shape = \" + str(y_cv.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaler_final.data_min_ = [87.91394]\n",
      "scaler_final.data_max_ = [144.956665]\n"
     ]
    }
   ],
   "source": [
    "# Here we scale the train_cv set, for the final model\n",
    "scaler_final = MinMaxScaler(feature_range=(0, 1))\n",
    "train_cv_scaled_final = scaler_final.fit_transform(np.array(train_cv['adj_close']).reshape(-1,1))\n",
    "print(\"scaler_final.data_min_ = \" + str(scaler_final.data_min_))\n",
    "print(\"scaler_final.data_max_ = \" + str(scaler_final.data_max_))\n",
    "\n",
    "# Scale the test dataset according the min and max obtained from train_cv set\n",
    "test_scaled  = scaler_final.transform(np.array(df['adj_close']).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create and fit the LSTM network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      " - 11s - loss: 0.0106\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a2f4858d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the LSTM network\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=lstm_units, return_sequences=True, input_shape=(x_train.shape[1],1)))\n",
    "model.add(Dropout(dropout_prob)) # Add dropout with a probability of 0.5\n",
    "model.add(LSTM(units=lstm_units))\n",
    "model.add(Dropout(dropout_prob)) # Add dropout with a probability of 0.5\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer=optimizer)\n",
    "model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 9, 50)             10400     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 9, 50)             0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 30,651\n",
      "Trainable params: 30,651\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Print model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot model and save to file\n",
    "plot_model(model, to_file='model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict on dev set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 2.185\n",
      "MAPE = 1.063%\n"
     ]
    }
   ],
   "source": [
    "# Do prediction\n",
    "est = model.predict(x_cv)\n",
    "est_inv = scaler.inverse_transform(est)\n",
    "\n",
    "# Get correct scale of y_cv\n",
    "y_cv_inv = scaler.inverse_transform(y_cv)\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse_bef_tuning = math.sqrt(mean_squared_error(y_cv_inv, est_inv))\n",
    "print(\"RMSE = %0.3f\" % rmse_bef_tuning)\n",
    "\n",
    "# Calculate MAPE\n",
    "mape_pct_bef_tuning = get_mape(y_cv_inv, est_inv)\n",
    "print(\"MAPE = %0.3f%%\" % mape_pct_bef_tuning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'USD')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0XpsQO4ElS5awt9DMtJ2/fj4A2VnZfn93v7yj1jpgP0AasLHqeChwENhV9ePE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\nsblxwctxuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "est_df = pd.DataFrame({'est_inv': est_inv.reshape(-1), \n",
    "                       'y_cv_inv': y_cv_inv.reshape(-1),\n",
    "                       'date': cv['date']})\n",
    "\n",
    "ax = train.plot(x='date', y='adj_close', style='b-', grid=True)\n",
    "ax = cv.plot(x='date', y='adj_close', style='y-', grid=True, ax=ax)\n",
    "ax = test.plot(x='date', y='adj_close', style='g-', grid=True, ax=ax)\n",
    "ax = est_df.plot(x='date', y='est_inv', style='r-', grid=True, ax=ax)\n",
    "ax.legend(['train', 'dev', 'test', 'est'])\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning N (no. of days to use as features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e148975fc870471d99599f4ccb22c911",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Minutes taken = 37.21444189945857\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3.402154</td>\n",
       "      <td>2.065506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4.954535</td>\n",
       "      <td>3.342947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.463920</td>\n",
       "      <td>1.219989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1.923303</td>\n",
       "      <td>1.012130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6.349972</td>\n",
       "      <td>4.447110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>3.398296</td>\n",
       "      <td>1.988835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>2.701772</td>\n",
       "      <td>1.664489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>2.480629</td>\n",
       "      <td>1.505606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>5.819515</td>\n",
       "      <td>3.931865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>3.316198</td>\n",
       "      <td>2.180850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>2.347754</td>\n",
       "      <td>1.151884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>2.277515</td>\n",
       "      <td>1.214217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>2.705742</td>\n",
       "      <td>1.647728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>2.813777</td>\n",
       "      <td>1.727719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>4.139463</td>\n",
       "      <td>2.842839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>6.303033</td>\n",
       "      <td>4.433774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>2.496130</td>\n",
       "      <td>1.476541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>4.022248</td>\n",
       "      <td>2.742736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>2.230084</td>\n",
       "      <td>1.139145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21</td>\n",
       "      <td>4.510267</td>\n",
       "      <td>3.007639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>22</td>\n",
       "      <td>3.888046</td>\n",
       "      <td>2.646871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>2.412949</td>\n",
       "      <td>1.285624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24</td>\n",
       "      <td>3.491575</td>\n",
       "      <td>2.305346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>2.273186</td>\n",
       "      <td>1.102695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>2.804253</td>\n",
       "      <td>1.749790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27</td>\n",
       "      <td>2.614645</td>\n",
       "      <td>1.608935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28</td>\n",
       "      <td>5.861704</td>\n",
       "      <td>4.076812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29</td>\n",
       "      <td>2.518075</td>\n",
       "      <td>1.524070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>4.054238</td>\n",
       "      <td>2.667347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31</td>\n",
       "      <td>3.848969</td>\n",
       "      <td>2.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32</td>\n",
       "      <td>4.653523</td>\n",
       "      <td>3.174030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>33</td>\n",
       "      <td>3.502195</td>\n",
       "      <td>2.290386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>34</td>\n",
       "      <td>2.461438</td>\n",
       "      <td>1.336409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>35</td>\n",
       "      <td>3.955594</td>\n",
       "      <td>2.601117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>36</td>\n",
       "      <td>2.248791</td>\n",
       "      <td>1.141291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>37</td>\n",
       "      <td>4.422073</td>\n",
       "      <td>2.992810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>38</td>\n",
       "      <td>2.372153</td>\n",
       "      <td>1.092980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>39</td>\n",
       "      <td>4.665751</td>\n",
       "      <td>3.183334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>40</td>\n",
       "      <td>2.446237</td>\n",
       "      <td>1.406703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>41</td>\n",
       "      <td>3.004754</td>\n",
       "      <td>1.675239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>42</td>\n",
       "      <td>4.228044</td>\n",
       "      <td>2.832976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>43</td>\n",
       "      <td>3.538025</td>\n",
       "      <td>2.264597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>44</td>\n",
       "      <td>2.312162</td>\n",
       "      <td>1.166870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>45</td>\n",
       "      <td>2.255915</td>\n",
       "      <td>1.204273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>46</td>\n",
       "      <td>2.739489</td>\n",
       "      <td>1.678152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>47</td>\n",
       "      <td>2.364214</td>\n",
       "      <td>1.255990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>48</td>\n",
       "      <td>2.268121</td>\n",
       "      <td>1.099953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>49</td>\n",
       "      <td>2.277470</td>\n",
       "      <td>1.129648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>50</td>\n",
       "      <td>2.333485</td>\n",
       "      <td>1.257695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>51</td>\n",
       "      <td>2.349229</td>\n",
       "      <td>1.089004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>52</td>\n",
       "      <td>6.097120</td>\n",
       "      <td>4.192696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>53</td>\n",
       "      <td>2.341167</td>\n",
       "      <td>1.270030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>54</td>\n",
       "      <td>3.256757</td>\n",
       "      <td>2.174401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>55</td>\n",
       "      <td>2.609314</td>\n",
       "      <td>1.546172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>56</td>\n",
       "      <td>2.896178</td>\n",
       "      <td>1.568575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>57</td>\n",
       "      <td>2.867951</td>\n",
       "      <td>1.813302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>58</td>\n",
       "      <td>2.977016</td>\n",
       "      <td>1.953033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>59</td>\n",
       "      <td>2.374443</td>\n",
       "      <td>1.242241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     N      rmse  mape_pct\n",
       "0    2  3.402154  2.065506\n",
       "1    3  4.954535  3.342947\n",
       "2    4  2.463920  1.219989\n",
       "3    5  1.923303  1.012130\n",
       "4    6  6.349972  4.447110\n",
       "5    7  3.398296  1.988835\n",
       "6    8  2.701772  1.664489\n",
       "7    9  2.480629  1.505606\n",
       "8   10  5.819515  3.931865\n",
       "9   11  3.316198  2.180850\n",
       "10  12  2.347754  1.151884\n",
       "11  13  2.277515  1.214217\n",
       "12  14  2.705742  1.647728\n",
       "13  15  2.813777  1.727719\n",
       "14  16  4.139463  2.842839\n",
       "15  17  6.303033  4.433774\n",
       "16  18  2.496130  1.476541\n",
       "17  19  4.022248  2.742736\n",
       "18  20  2.230084  1.139145\n",
       "19  21  4.510267  3.007639\n",
       "20  22  3.888046  2.646871\n",
       "21  23  2.412949  1.285624\n",
       "22  24  3.491575  2.305346\n",
       "23  25  2.273186  1.102695\n",
       "24  26  2.804253  1.749790\n",
       "25  27  2.614645  1.608935\n",
       "26  28  5.861704  4.076812\n",
       "27  29  2.518075  1.524070\n",
       "28  30  4.054238  2.667347\n",
       "29  31  3.848969  2.538462\n",
       "30  32  4.653523  3.174030\n",
       "31  33  3.502195  2.290386\n",
       "32  34  2.461438  1.336409\n",
       "33  35  3.955594  2.601117\n",
       "34  36  2.248791  1.141291\n",
       "35  37  4.422073  2.992810\n",
       "36  38  2.372153  1.092980\n",
       "37  39  4.665751  3.183334\n",
       "38  40  2.446237  1.406703\n",
       "39  41  3.004754  1.675239\n",
       "40  42  4.228044  2.832976\n",
       "41  43  3.538025  2.264597\n",
       "42  44  2.312162  1.166870\n",
       "43  45  2.255915  1.204273\n",
       "44  46  2.739489  1.678152\n",
       "45  47  2.364214  1.255990\n",
       "46  48  2.268121  1.099953\n",
       "47  49  2.277470  1.129648\n",
       "48  50  2.333485  1.257695\n",
       "49  51  2.349229  1.089004\n",
       "50  52  6.097120  4.192696\n",
       "51  53  2.341167  1.270030\n",
       "52  54  3.256757  2.174401\n",
       "53  55  2.609314  1.546172\n",
       "54  56  2.896178  1.568575\n",
       "55  57  2.867951  1.813302\n",
       "56  58  2.977016  1.953033\n",
       "57  59  2.374443  1.242241"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_label = 'N'\n",
    "param_list = range(2, 60)\n",
    "\n",
    "error_rate = {param_label: [], 'rmse': [], 'mape_pct': []}\n",
    "tic = time.time()\n",
    "for param in tqdm_notebook(param_list):\n",
    "    \n",
    "    # Split train into x and y\n",
    "    x_train_scaled, y_train_scaled = get_x_y(train_scaled, param, param)\n",
    "\n",
    "    # Split cv into x and y\n",
    "    x_cv_scaled, y_cv_scaled = get_x_y(train_cv_scaled, param, num_train)\n",
    "    \n",
    "    # Train, predict and eval model\n",
    "    rmse, mape, _ = train_pred_eval_model(x_train_scaled, \\\n",
    "                                          y_train_scaled, \\\n",
    "                                          x_cv_scaled, \\\n",
    "                                          y_cv_scaled, \\\n",
    "                                          scaler, \\\n",
    "                                          lstm_units=lstm_units, \\\n",
    "                                          dropout_prob=dropout_prob, \\\n",
    "                                          optimizer='adam', \\\n",
    "                                          epochs=epochs, \\\n",
    "                                          batch_size=batch_size)\n",
    "    \n",
    "    # Collect results\n",
    "    error_rate[param_label].append(param)\n",
    "    error_rate['rmse'].append(rmse)\n",
    "    error_rate['mape_pct'].append(mape)\n",
    "    \n",
    "error_rate = pd.DataFrame(error_rate)\n",
    "toc = time.time()\n",
    "print(\"Minutes taken = \" + str((toc-tic)/60.0))\n",
    "error_rate   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'RMSE/MAPE(%)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X0ECCS8SWIFSGNWerqYn+5LxKFxFbCZM1Z2tqitpkdbbqIYxoF1sNDTRNWjqk5GFnC6Dj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D5tbjuUE69s6ZYRfZQtGZCKioC77/ZfUSxZwjH8+OP13oYnJBgqyjKi1yTj5P2TYFObSd5X2dIt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\nrKzs7behBcMwsHXrVtTX12OK9FQEuBgZN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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot RMSE \n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "ax = error_rate.plot(x='N', y='rmse', style='bx-', grid=True)\n",
    "ax = error_rate.plot(x='N', y='mape_pct', style='rx-', grid=True, ax=ax)\n",
    "ax.set_xlabel(\"N\")\n",
    "ax.set_ylabel(\"RMSE/MAPE(%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min RMSE = 1.923\n",
      "min MAPE = 1.012%\n",
      "optimum N = 5\n"
     ]
    }
   ],
   "source": [
    "# Get optimum value for param\n",
    "temp = error_rate[error_rate['rmse'] == error_rate['rmse'].min()]\n",
    "N_opt = temp['N'].values[0]\n",
    "print(\"min RMSE = %0.3f\" % error_rate['rmse'].min())\n",
    "print(\"min MAPE = %0.3f%%\" % error_rate['mape_pct'].min())\n",
    "print(\"optimum \" + param_label + \" = \" + str(N_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning model - epochs and batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d96d7f1c07e4e73b56a79936f10503e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e9bd02bd8fd4a298170c23df010edde",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c1de7d347f346cdaabaee143532ab6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fdb7eba3c07d4b50bd154ab9444b3724",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c22680d07567435280861ed7b7be4141",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc751a47f7c74bde9def6a9fefd6d42f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1fbeed317fd43319eaef72a6563c9c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minutes taken = 22.18071840206782\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epochs</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4.068730</td>\n",
       "      <td>2.759774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2.192115</td>\n",
       "      <td>1.203321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>22.989611</td>\n",
       "      <td>16.910898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>34.019504</td>\n",
       "      <td>25.111611</td>\n",
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       "    <tr>\n",
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       "      <td>128</td>\n",
       "      <td>41.256545</td>\n",
       "      <td>30.480860</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2.543338</td>\n",
       "      <td>1.370761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>2.668650</td>\n",
       "      <td>1.349841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>2.823363</td>\n",
       "      <td>1.457263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>3.518908</td>\n",
       "      <td>2.397459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>128</td>\n",
       "      <td>10.185083</td>\n",
       "      <td>7.362182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>1.886783</td>\n",
       "      <td>0.994164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>2.049614</td>\n",
       "      <td>0.961238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>2.680748</td>\n",
       "      <td>1.377350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "      <td>2.618268</td>\n",
       "      <td>1.297625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20</td>\n",
       "      <td>128</td>\n",
       "      <td>1.940348</td>\n",
       "      <td>1.043235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>2.052148</td>\n",
       "      <td>1.197907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>2.005873</td>\n",
       "      <td>1.120583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>1.987215</td>\n",
       "      <td>0.900961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>2.717141</td>\n",
       "      <td>1.368078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30</td>\n",
       "      <td>128</td>\n",
       "      <td>2.541818</td>\n",
       "      <td>1.242847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>2.327970</td>\n",
       "      <td>1.471474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>1.994414</td>\n",
       "      <td>1.125569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>1.865434</td>\n",
       "      <td>0.899372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>64</td>\n",
       "      <td>2.155059</td>\n",
       "      <td>0.992109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>128</td>\n",
       "      <td>2.793682</td>\n",
       "      <td>1.432134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>1.807929</td>\n",
       "      <td>1.019804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>50</td>\n",
       "      <td>16</td>\n",
       "      <td>1.790136</td>\n",
       "      <td>0.929282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>1.893657</td>\n",
       "      <td>0.921258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>2.146433</td>\n",
       "      <td>0.981773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>50</td>\n",
       "      <td>128</td>\n",
       "      <td>2.118397</td>\n",
       "      <td>0.969347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epochs  batch_size       rmse   mape_pct\n",
       "0        1           8   4.068730   2.759774\n",
       "1        1          16   2.192115   1.203321\n",
       "2        1          32  22.989611  16.910898\n",
       "3        1          64  34.019504  25.111611\n",
       "4        1         128  41.256545  30.480860\n",
       "5       10           8   2.543338   1.370761\n",
       "6       10          16   2.668650   1.349841\n",
       "7       10          32   2.823363   1.457263\n",
       "8       10          64   3.518908   2.397459\n",
       "9       10         128  10.185083   7.362182\n",
       "10      20           8   1.886783   0.994164\n",
       "11      20          16   2.049614   0.961238\n",
       "12      20          32   2.680748   1.377350\n",
       "13      20          64   2.618268   1.297625\n",
       "14      20         128   1.940348   1.043235\n",
       "15      30           8   2.052148   1.197907\n",
       "16      30          16   2.005873   1.120583\n",
       "17      30          32   1.987215   0.900961\n",
       "18      30          64   2.717141   1.368078\n",
       "19      30         128   2.541818   1.242847\n",
       "20      40           8   2.327970   1.471474\n",
       "21      40          16   1.994414   1.125569\n",
       "22      40          32   1.865434   0.899372\n",
       "23      40          64   2.155059   0.992109\n",
       "24      40         128   2.793682   1.432134\n",
       "25      50           8   1.807929   1.019804\n",
       "26      50          16   1.790136   0.929282\n",
       "27      50          32   1.893657   0.921258\n",
       "28      50          64   2.146433   0.981773\n",
       "29      50         128   2.118397   0.969347"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_label = 'epochs'\n",
    "param_list = [1, 10, 20, 30, 40, 50]\n",
    "\n",
    "param2_label = 'batch_size'\n",
    "param2_list = [8, 16, 32, 64, 128]\n",
    "\n",
    "# Split train into x and y\n",
    "x_train_scaled, y_train_scaled = get_x_y(train_scaled, N_opt, N_opt)\n",
    "\n",
    "# Split cv into x and y\n",
    "x_cv_scaled, y_cv_scaled = get_x_y(train_cv_scaled, N_opt, num_train)\n",
    "\n",
    "error_rate = {param_label: [], param2_label: [], 'rmse': [], 'mape_pct': []}\n",
    "tic = time.time()\n",
    "for param in tqdm_notebook(param_list):\n",
    "    \n",
    "    for param2 in tqdm_notebook(param2_list):\n",
    "    \n",
    "        # Train, predict and eval model\n",
    "        rmse, mape, _ = train_pred_eval_model(x_train_scaled, \\\n",
    "                                              y_train_scaled, \\\n",
    "                                              x_cv_scaled, \\\n",
    "                                              y_cv_scaled, \\\n",
    "                                              scaler, \\\n",
    "                                              lstm_units=lstm_units, \\\n",
    "                                              dropout_prob=dropout_prob, \\\n",
    "                                              optimizer='adam', \\\n",
    "                                              epochs=param, \\\n",
    "                                              batch_size=param2)\n",
    "    \n",
    "        # Collect results\n",
    "        error_rate[param_label].append(param)\n",
    "        error_rate[param2_label].append(param2)\n",
    "        error_rate['rmse'].append(rmse)\n",
    "        error_rate['mape_pct'].append(mape)\n",
    "    \n",
    "error_rate = pd.DataFrame(error_rate)\n",
    "toc = time.time()\n",
    "print(\"Minutes taken = \" + str((toc-tic)/60.0))\n",
    "error_rate   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aa9265d30>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w9Ax0yqpClccrUbaf3YiJiIiIyLVYDLSQEB5QlCQUFHwIh6NatbiXuhFncqoQEREREbkW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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot performance versus params\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "temp = error_rate[error_rate[param2_label]==param2_list[0]]\n",
    "ax = temp.plot(x=param_label, y='rmse', style='bs-', grid=True)\n",
    "legend_list = [param2_label + '_' + str(param2_list[0])]\n",
    "\n",
    "color_list = ['r', 'g', 'k', 'y', 'm', 'c', '0.75']\n",
    "for i in range(1,len(param2_list)):\n",
    "    temp = error_rate[error_rate[param2_label]==param2_list[i]]\n",
    "    ax = temp.plot(x=param_label, y='rmse', color=color_list[i%len(color_list)], marker='s', grid=True, ax=ax)\n",
    "    legend_list.append(param2_label + '_' + str(param2_list[i]))\n",
    "\n",
    "ax.set_xlabel(param_label)\n",
    "ax.set_ylabel(\"RMSE\")\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "plt.legend(legend_list, loc='center left', bbox_to_anchor=(1.0, 0.5)) # positions legend outside figure\n",
    "# ax.set_xlim([10, 50])\n",
    "# ax.set_ylim([0, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min RMSE = 1.790\n",
      "min MAPE = 0.899%\n",
      "optimum epochs = 50\n",
      "optimum batch_size = 16\n"
     ]
    }
   ],
   "source": [
    "# Get optimum value for param and param2\n",
    "temp = error_rate[error_rate['rmse'] == error_rate['rmse'].min()]\n",
    "epochs_opt = temp[param_label].values[0]\n",
    "batch_size_opt = temp[param2_label].values[0]\n",
    "print(\"min RMSE = %0.3f\" % error_rate['rmse'].min())\n",
    "print(\"min MAPE = %0.3f%%\" % error_rate['mape_pct'].min())\n",
    "print(\"optimum \" + param_label + \" = \" + str(epochs_opt))\n",
    "print(\"optimum \" + param2_label + \" = \" + str(batch_size_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning model - LSTM units and dropout prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5922c8b3b3b454092b7c9854c20538b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a49df0e6c3684cc0a51b9549ed1133ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af0afca9ed5041698df778f11052afb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "218b3833fb5f4b4cb0a2c7064f360466",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cf798a4b7a74ac29e0d6f99ec2e4b46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minutes taken = 31.418873314062754\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lstm_units</th>\n",
       "      <th>dropout_prob</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.116626</td>\n",
       "      <td>2.715886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.632733</td>\n",
       "      <td>3.169495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5.294275</td>\n",
       "      <td>3.700561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.679434</td>\n",
       "      <td>6.238321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0.9</td>\n",
       "      <td>14.513627</td>\n",
       "      <td>10.527232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.245515</td>\n",
       "      <td>2.039597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.964435</td>\n",
       "      <td>1.944805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.172456</td>\n",
       "      <td>1.267783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.788188</td>\n",
       "      <td>1.786610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.988050</td>\n",
       "      <td>1.947303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>0.9</td>\n",
       "      <td>6.103492</td>\n",
       "      <td>4.302422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.040189</td>\n",
       "      <td>1.185081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.250986</td>\n",
       "      <td>3.691928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.948893</td>\n",
       "      <td>1.045492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>64</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.601620</td>\n",
       "      <td>1.645926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>64</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.176364</td>\n",
       "      <td>2.107000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>64</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.426160</td>\n",
       "      <td>3.035940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.842961</td>\n",
       "      <td>0.875073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>128</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.240859</td>\n",
       "      <td>1.364056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>128</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.977417</td>\n",
       "      <td>2.730878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>128</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.168836</td>\n",
       "      <td>2.883845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>128</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.464592</td>\n",
       "      <td>1.526599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>128</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.654749</td>\n",
       "      <td>2.451933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.702006</td>\n",
       "      <td>0.797517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    lstm_units  dropout_prob       rmse   mape_pct\n",
       "0           10           0.5   4.116626   2.715886\n",
       "1           10           0.6   4.632733   3.169495\n",
       "2           10           0.7   5.294275   3.700561\n",
       "3           10           0.8   8.679434   6.238321\n",
       "4           10           0.9  14.513627  10.527232\n",
       "5           10           1.0   3.245515   2.039597\n",
       "6           50           0.5   2.964435   1.944805\n",
       "7           50           0.6   2.172456   1.267783\n",
       "8           50           0.7   2.788188   1.786610\n",
       "9           50           0.8   2.988050   1.947303\n",
       "10          50           0.9   6.103492   4.302422\n",
       "11          50           1.0   2.040189   1.185081\n",
       "12          64           0.5   5.250986   3.691928\n",
       "13          64           0.6   1.948893   1.045492\n",
       "14          64           0.7   2.601620   1.645926\n",
       "15          64           0.8   3.176364   2.107000\n",
       "16          64           0.9   4.426160   3.035940\n",
       "17          64           1.0   1.842961   0.875073\n",
       "18         128           0.5   2.240859   1.364056\n",
       "19         128           0.6   3.977417   2.730878\n",
       "20         128           0.7   4.168836   2.883845\n",
       "21         128           0.8   2.464592   1.526599\n",
       "22         128           0.9   3.654749   2.451933\n",
       "23         128           1.0   1.702006   0.797517"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_label = 'lstm_units'\n",
    "param_list = [10, 50, 64, 128]\n",
    "\n",
    "param2_label = 'dropout_prob'\n",
    "param2_list = [0.5, 0.6, 0.7, 0.8, 0.9, 1]\n",
    "\n",
    "error_rate = {param_label: [], param2_label: [], 'rmse': [], 'mape_pct': []}\n",
    "tic = time.time()\n",
    "for param in tqdm_notebook(param_list):\n",
    "    \n",
    "    for param2 in tqdm_notebook(param2_list):\n",
    "    \n",
    "        # Train, predict and eval model\n",
    "        rmse, mape, _ = train_pred_eval_model(x_train_scaled, \\\n",
    "                                              y_train_scaled, \\\n",
    "                                              x_cv_scaled, \\\n",
    "                                              y_cv_scaled, \\\n",
    "                                              scaler, \\\n",
    "                                              lstm_units=param, \\\n",
    "                                              dropout_prob=param2, \\\n",
    "                                              optimizer='adam', \\\n",
    "                                              epochs=epochs_opt, \\\n",
    "                                              batch_size=batch_size_opt)\n",
    "    \n",
    "        # Collect results\n",
    "        error_rate[param_label].append(param)\n",
    "        error_rate[param2_label].append(param2)\n",
    "        error_rate['rmse'].append(rmse)\n",
    "        error_rate['mape_pct'].append(mape)\n",
    "    \n",
    "error_rate = pd.DataFrame(error_rate)\n",
    "toc = time.time()\n",
    "print(\"Minutes taken = \" + str((toc-tic)/60.0))\n",
    "error_rate   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ac610c080>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YbFCHnL2dEbEpApWHK3Fh3QW54xARERFRH8NigzpleNgAr4e8cO65c6jOYDsVEREREdmO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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot performance versus params\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "temp = error_rate[error_rate[param2_label]==param2_list[0]]\n",
    "ax = temp.plot(x=param_label, y='rmse', style='bs-', grid=True)\n",
    "legend_list = [param2_label + '_' + str(param2_list[0])]\n",
    "\n",
    "color_list = ['r', 'g', 'k', 'y', 'm', 'c', '0.75']\n",
    "for i in range(1,len(param2_list)):\n",
    "    temp = error_rate[error_rate[param2_label]==param2_list[i]]\n",
    "    ax = temp.plot(x=param_label, y='rmse', color=color_list[i%len(color_list)], marker='s', grid=True, ax=ax)\n",
    "    legend_list.append(param2_label + '_' + str(param2_list[i]))\n",
    "\n",
    "ax.set_xlabel(param_label)\n",
    "ax.set_ylabel(\"RMSE\")\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "plt.legend(legend_list, loc='center left', bbox_to_anchor=(1.0, 0.5)) # positions legend outside figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min RMSE = 1.702\n",
      "min MAPE = 0.798%\n",
      "optimum lstm_units = 128\n",
      "optimum dropout_prob = 1.0\n"
     ]
    }
   ],
   "source": [
    "# Get optimum value for param and param2\n",
    "temp = error_rate[error_rate['rmse'] == error_rate['rmse'].min()]\n",
    "lstm_units_opt = temp[param_label].values[0]\n",
    "dropout_prob_opt = temp[param2_label].values[0]\n",
    "print(\"min RMSE = %0.3f\" % error_rate['rmse'].min())\n",
    "print(\"min MAPE = %0.3f%%\" % error_rate['mape_pct'].min())\n",
    "print(\"optimum \" + param_label + \" = \" + str(lstm_units_opt))\n",
    "print(\"optimum \" + param2_label + \" = \" + str(dropout_prob_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning model - optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df21638e50624502aa95e8480c1705aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minutes taken = 12.38256831963857\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optimizer</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adam</td>\n",
       "      <td>1.649932</td>\n",
       "      <td>0.835126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sgd</td>\n",
       "      <td>6.413210</td>\n",
       "      <td>4.508988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rmsprop</td>\n",
       "      <td>1.733020</td>\n",
       "      <td>0.915127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adagrad</td>\n",
       "      <td>1.856629</td>\n",
       "      <td>0.890655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adadelta</td>\n",
       "      <td>2.009798</td>\n",
       "      <td>1.151585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>adamax</td>\n",
       "      <td>1.805665</td>\n",
       "      <td>0.939247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nadam</td>\n",
       "      <td>1.882594</td>\n",
       "      <td>1.140058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  optimizer      rmse  mape_pct\n",
       "0      adam  1.649932  0.835126\n",
       "1       sgd  6.413210  4.508988\n",
       "2   rmsprop  1.733020  0.915127\n",
       "3   adagrad  1.856629  0.890655\n",
       "4  adadelta  2.009798  1.151585\n",
       "5    adamax  1.805665  0.939247\n",
       "6     nadam  1.882594  1.140058"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_label = 'optimizer'\n",
    "param_list = ['adam', 'sgd', 'rmsprop', 'adagrad', 'adadelta', 'adamax', 'nadam']\n",
    "\n",
    "error_rate = {param_label: [], 'rmse': [], 'mape_pct': []}\n",
    "tic = time.time()\n",
    "for param in tqdm_notebook(param_list):\n",
    "   \n",
    "    # Train, predict and eval model\n",
    "    rmse, mape, _ = train_pred_eval_model(x_train_scaled, \\\n",
    "                                              y_train_scaled, \\\n",
    "                                              x_cv_scaled, \\\n",
    "                                              y_cv_scaled, \\\n",
    "                                              scaler, \\\n",
    "                                              lstm_units=lstm_units_opt, \\\n",
    "                                              dropout_prob=dropout_prob_opt, \\\n",
    "                                              optimizer=param, \\\n",
    "                                              epochs=epochs_opt, \\\n",
    "                                              batch_size=batch_size_opt)\n",
    "    \n",
    "    # Collect results\n",
    "    error_rate[param_label].append(param)\n",
    "    error_rate['rmse'].append(rmse)\n",
    "    error_rate['mape_pct'].append(mape)\n",
    "    \n",
    "error_rate = pd.DataFrame(error_rate)\n",
    "toc = time.time()\n",
    "print(\"Minutes taken = \" + str((toc-tic)/60.0))\n",
    "error_rate   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'RMSE/MAPE(%)')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ii+7OSiIiInGqM4QZY/4LWAV8DXgOOBc4EegHjACuB5oBzxljnjbGHB3abKWG\n2PvBdu+GJ56ASZOgoODg7/NLks8+G3tJ8tNP3eqhiIhInNJZCRsJjLHWDrPW3mitfcZa+6a19j1r\n7avW2ruttV8FugBPAGNCnbFUEXs/2KxZsHXroUuRPr8k+cQT4c/rINQXJiIimaLOEGatTVhrl6Tx\nvl3W2tuttX8NZmpSl4zoB0smoU0bGDeu7vcOH+4264qxJNmtm7uXpPrCREQkbg2+OtIY08wYM8AY\nM8gYE9c6TF6bPTvmfrC9e+Gxx+Dss9Pbqr9ySXLr1vDndxAlJa6Xbu/e2KYgIiLSsBBmjBmB6xMr\nBV4E1hhj0lgKkSClUlBYGGM/2Jw5buOtdEqRvkSioo8sJp4H27bBokWxTUFERKTBK2H/P/BVa21n\n4DDg58Adgc1K0hJ7P1gy6VLghAnpf83w4XDEEbGWJP2VQ/WFiYhInNLdomK2MaZ/pZdaA28AWGvt\n/v9uG/z05GC2bIH//CfGUmR5OUyfDmeeCa1bp/91TZq4kuQzz8RWkuzUCQYOVF+YiIjEK92VsP8F\nnjLGXGuMaQbcBvzHGPMvY8x0YAbwu7AmKTXF3g82bx5s2OACVX35Jcknnwx+XmkqKYG5c900RERE\n4pBWCLPWPgkMBjoBrwFvAmcAL+F6wk631v4mrElKTX4/2LC4tshNJqF5c9eUX1+nnAI9esRakvQ8\n+PxzePXV2KYgIiJ5rlm6b7TWbgd+YIw5BbgTeBn4H2vttrAmJwcXaz+Yta4UOW4ctG1AFdovSd55\nJ3z2WcOO0UhjxriLNWfNglNPjXx4ERGR9BvzjTGHGWOGAMuAocB64HVjzLlhTU5q9/HHMe8PtmgR\nrF5dv6siq0skYNeu2EqSHTrAiSeqL0xEROKTbmP+l4APcL1fq4GzrLW/BM4CfmiMSRpjuoY3Tals\n9mz3GFsISyahaVN3q6KGGjEi9pJkSYlrbdu5M7YpiIhIHkt3Jewm4GvW2q7A6cCNANbad6y1JcBM\nXHlSIhBrP5i1LoR5Hhx+eMOP06SJW0l7+mlXkoyB57nFuHnzYhleRETyXLohrA2wfP9/vw+0qvzJ\n/bcqOiXAeckhpFIwalRM/WBLlsC77zauFOnzS5IzZjT+WA0werRb0NN+YSIiEod0Q9i9wAxjzD+A\nV4H7q7/BWrspyIlJ7T7+OOb9wZJJ19E+eXLjjzVyJHTvHltJsm1bGDJEfWEiIhKPdLeo+DHwLeB1\n4HvW2l+EOis5qNj7waZNc5cTdg2gBdAvSc6cCdu3N/54DVBS4rap2LEjluFFRCSPpX11pLX2SWvt\n/1prnw1zQnJosfaDLV8OS5e25JG3AAAgAElEQVQ2bIPWg0kkXGf8U08Fd8x68DzYs8dt3CoiIhKl\nOkOYMeb/M8aYdA5mjCk2xoxO871XG2OsMeZP6bxfHL8frHnzGAZPJt3jlCnBHXPUKOjWLbaS5KhR\nUFCgvjAREYleOithXwOW7w9NA6sHsv37h00yxjyM6xdrV9cB92/4+k3gPw2ZdL7KiH4w/wbcQfFL\nkv/+dywlydat4eST1RcmIiLRqzOE7d+C4r+AMbgbdW83xqw0xiwzxmwEPgL+ArwHDLDWHrKuZIxp\nBzwIfB34pJHzzysvvugeYwlhK1fCa68Fc1VkdX5JMqarJEtK3P6zMe2UISIieSrdxvwZ1trxQBfg\nYuBW3BWTPwNOBnpYa6+21n6UxuH+D5hmrVUBqJ5i7QebPt09hhHCRo1yjf4xlSQ9D/btgzlzYhle\nRETyVNr3jgSw1m4GHmvoYMaYbwJHAZek8d7LgMsAunTpQiqVauiwadm+fXvoYzTWjBlDOe643bz8\ncvRV3MH33EOTo45i0Zo1sGZNWl9Tn3N69Cmn0PWpp5g7cyblhYWNmGn97dnThIKCU7n33nW0bv1+\npGPXVzZ8n2YTnc/g6ZwGS+czeBl1Tq21dX4AXwCaVXpeVO3zLXE76h/qGP1xpctjKr2WAv5U1/hD\nhgyxYSstLQ19jMb46CNrwdpf/SqGwT/4wA1+4431+rJ6ndNUyo3xr3/Vb24BGTvW2sGDYxm6XjL9\n+zTb6HwGT+c0WDqfwYvinAILbRr5Kt0tKmYCh1V6vs4Y06fS83bAXXUcYwTQEVhijNlrjNmL6zP7\nzv7ncez/njVi3R8szFKk79RToUuX2EqSJSWweDFs2RLL8CIikofSDWHVt6hIa8uKah4DjgdOrPSx\nEHho/3/vbsAx80YqBa1awdChMQyeTMJxx8Gxx4Y3RtOmLuTNmBHLzqme526L6YddERGRsKW9WWtj\nWWs/tdYuqfwB7AC27H9uo5pLNoptf7BNm1zHepirYL5EAsrK3HYVETv5ZBdytV+YiIhEJbIQJg23\neTO8+WZMpcjHHoPy8mhC2OjRsZUkmzd3IVf7hYmISFTqc3XkIGOM3zFjgAHGmPb7n3dsyODW2rEN\n+bp8E2s/WDIJffvCoEHhj9W0qduN/9574fPP3dJUhEpK4Kc/hY8+gk6dIh1aRETyUH1Wwp7B9XAt\nBFoBj1d6/nTwUxNfaWlM/WCffOLqc+efD+nduarxEgkXwGIoSXqee8yUK5dFRCS3pRvCegN99j8e\n7KPPQb9aGiW2frAnnoC9e6MpRfpOOw06d46lJDlkCBQVqS9MRESikVY50lq7OuyJSO0++giWLIGL\nLoph8GQSjjwy2i36/ZLkffdFXpJs1sy1pakvTEREopDWSpgxppUx5s/GmHXGmE3GmH8YYxrUByb1\n4/eD+aWyyHz2GTz7rAtEUZUifX5JcubMaMfF9YUtXw7r10c+tIiI5Jl0y5E/B74CzMDt6zUOuCOk\nOUklse0PNmMG7NoFU6dGPDCuJNmpUywlSfWFiYhIVNINYVOAr1trL7PW/gCYCEw2xjQNb2oCLgyc\neioUFEQ8cDLpbqo9cmTEA+PqglOmwFNPuX3DInTCCdC+vfrCREQkfOmGsCOBOf4Ta+2rwF6gexiT\nEsfvB4t8awq/FHjeedAkpq3kEgm3c37EJcmmTWHMGPWFiYhI+NL9F7YpNW8rtJf67TMm9RTb/mBP\nP+2CWJRXRVY3Zgx07BhLSbKkBFasgDVrIh9aRETySLohygAPGGN2VXqtJXCXMeZz/wVr7aQgJ5fv\nYusHSybh8MNdEIpLs2ZuJe4f/3AlycLCyIb2+8JKS+HSSyMbVkRE8ky6K2H3AuuBjyt9PACsrfaa\nBKi0NIZ+sF27XC/Wuee6IBQnvyT5dLR7AQ8Y4Bbh1BcmIiJhSnefsK+GPRGpatMmWLoULr444oGf\nf95tTxFnKdLneW5F7pFH3KpYRJo0cSXg0lKwNvodOkREJD8E0nVtjBlrjHkgiGOJE1s/WDIJbdvC\n6adHPHAt/JLkk09GfpVkSQmsXet6w0RERMLQ4BBmjOlqjPmpMeZd4HngiOCmJakUtG4dcT/Ynj3w\n2GMwaRK0aBHhwIeQSMD27fDMM5EOW7kvTEREJAz1CmHGmCbGmEnGmMeBNcAvgfuBI6y1Y0OYX96K\nZX+wVMrdtDsTSpE+z4PDDov8Ksn+/d02aeoLExGRsKR726KjjDE3A+uAPwCvAwOAcmCatXZjeFPM\nP34/WCylyNatYfz4iAc+hIKCipLkzp2RDWuMy39+X5iIiEjQ0l0JewvoCCSstX2ttTdYa98NcV55\nLZZ+sH374NFH4ayzIt0OIi2JBGzbFnlJsqQENm5095IUEREJWroh7G1gAnCuMWZQiPMRKvrBhgyJ\ncNC5c90SXCaVIn0lJbGUJNUXJiIiYUorhFlrBwGTgSJgtjHmP8aY//Y/Hdbk8lUs/WDJpGvGP+us\nCAdNU0EBTJ4MTzwRaUmyTx848kj1hYmISDjSbsy31i6w1n4b6IbrC5uEu53R34wx3zXGdAtpjnkl\nln6w8nKYPt31ghUVRThwPfglyWefjWxIvy8slXKnSEREJEj13qLCWltmrb3HWjsaOBZ4CbgWt3u+\nNNKLL7rHSEPYggXwwQeZWYr0nX46dOgQeUmypAQ2b3bBWEREJEiN2qzVWrvcWnslcCSQCGZK+S2W\nfrBp09zGqOecE+Gg9VS5JLlrV93vD4j6wkREJCyB7Jhvrd1jrX00iGPlu1QKRo+OsB/MWtcPdsYZ\nbqUpkyUS7pZKEZYke/Z0vWHqCxMRkaClu0/YZ+l8hD3ZXLdpE7z1VsSlyMWLYeXKzC5F+k4/Hdq3\ndyt3EfI8Vybety/SYUVEJMeldQNvoA2wGrgP0N30QhJLP1gy6e5Yfe65EQ7aQM2bu5Lko4+6kmRE\nt1YqKYG//Q3eeANOOimSIUVEJA+kW46cCCwErgQuAfYAD1lr7638EdYk80UqBW3aRPwPfTIJY8ZA\np04RDtoIiQRs3QrPPx/ZkOoLExGRMKS7T9hMa20Cd5Pup4CrgA3GmD8bYwaHOcF8Evn+YG+9BW+/\nnR2lSN8ZZ7iSZIRXSXbr5u4lqb4wEREJUr0a8621H1trb92/eeuZuC0qFhpjMryjO/PF0g+WTLrH\n886LcNBGat7clU4ffxx2745sWM+DOXNg797IhhQRkRxX76sjjTGtjTFfB24FTgbuB3YEPbF8k0q5\nx8hD2MiR0L17hIMGIJGATz+NtCRZUuL2il20KLIhRUQkx6Udwowxo40xfwc+BL4J3AN0s9Z+xVob\n3ZJEjoq8H+y991yneTaVIn3jxkG7dpGWJP1wrL4wEREJSrpbVCwHpgGbgWHW2lOstXdZa7eFOrs8\nEnk/mF+KzMYQ5pckH3ssspJkp04wcKD6wkREJDjproQdjbt597eA+donLFgffgjLllVchReJZBKG\nDoVevSIcNEB+SfKFFyIb0vNg7txIW9FERCSHpbtP2FdDnUWei3x/sDVr3P0ib7opogFDMG4ctG3r\nSpITJkQyZEkJ/PGP8OqrbtVSRESkMdINYU9Yaz8JdSZ5LPJ+sOnT3WM2liJ9LVpUlCT/8pdI6rhj\nxoAxri9MIUxERBor3XLkRmPMLGPMD40xxSHOJy/594tslm4kbqxkEo4/Ho4+OqIBQ5JIwCefRFaS\n7NABTjxRfWEiIhKMdENYL+Ah4AvAMmPMYmPMz40xuolLI/n9YJGVIjdudI1N2bwK5vvCFypKkhHx\nPJg3D3bujGxIERHJUenumL/RWvt/1tqJQCfgl0Af4DljzGpjzB+NMWcYY5qGOdlcFPn+YI8+Ctbm\nRghr0QImTXIlyT17IhmypMTdtnLevEiGExGRHFbvzVqttduttdOstZcAnYGvAeXAXcBmY8zFAc8x\np0XeDzZtGvTrBwMGRDRgyBIJ2LIlshrh6NHQtKn2CxMRkcardwirzFq7z1r7grX2h9ba3oAHvBPM\n1PJDpP1gmze7SzGnTnUd5rngC1+AoqLISpJt28KQIeoLExGRxkt3s9ZfG2NaVXp+ljGmsNLztsaY\n+6y1i621C8KYaC7auNHdPzuyUuTjj8O+fblRivS1bOlKko8+GllJ0vPcNhU7dLMuERFphHRXwv4H\naFPp+UNAt0rPCwGVIevJ3x8ssk1ak0koLobBgyMaMCJ+STKiGmFJict7c+dGMpyIiOSodENY9dpV\njtSy4pVKuUpaJJnIv+H1+efnTinSN358pCXJUaPctmTqCxMRkcZoVE+YNE6k/WBPPeWWb3KpFOlr\n2RLOOSeykmTr1nDyyeoLExGRxlEIi0nk/WDJJHTvDsOHRzRgxBIJ+Pjjij0/QuZ5sGgRfKY7poqI\nSAPVJ4R92xjzY2PMj3G3O/p6peffDmd6uSvS+0Vu3w5PPw1TpkCTHM3d48e7vT4iKkmWlLhrHObM\niWQ4ERHJQekWwtZQ9SbeG4Ev1fIeSVNpaYT9YP/+t9viPRdLkb7CQjj7bFeSvP320Gu8I0a4vWJL\nS2HixFCHEhGRHJXWv1TW2uKQ55F3Iu0HSyahUyc3YC5LJOChh9zJPeOMUIdq2dIFMfWFiYhIQ+Vo\nbSqzbdgAy5dHVIosK4MZM+C889xW77lswgTXNR9RSdLzYPFitzuGiIhIfaW7WesJxhiv2msXG2NW\nGGM2GWPuNMY0D2eKuSfS/cGefdbtKprLpUifX5KcPh327g19uJISdxvO2bNDH0pERHJQuithvwRO\n9Z8YY44D7gHeBf6J26j1fwKfXY5Kpdztb048MYLBkkno0CHCHWFjlkhU3J4pZCefDK1aab8wERFp\nmHRD2EnAc5WeXwi8Za0db639IfAj4ItBTy5XRdYPtns3PPGEu61PQUHIg2WICRNcMoqgJNm8udu4\nVX1hIiLSEOmGsMOBdZWenwY8Wel5CugZ0JxyWqT9YLNmwdat+VGK9LVqFWlJ0vNgyRL46KPQhxIR\nkRyTbgj7COgBYIxpCgwB5lf6fHOgPNip5aZI9wdLJt3eWePGRTBYBkkkXCqKoFmrpMQ9RrRHrIiI\n5JB0Q1gKuN4Y0we4Yv9rlTthjgNWBTet3BVZP9jevfDYY25VqGXLkAfLMGedFVlJcsgQt9+b+sJE\nRKS+0g1h1wJHA+8BvwKutNbuqPT5S4AXAp5bTiotjagfbPZs16A+dWrIA2WgVq3cDqrTp7tt7UPU\nrJn7+1RfmIiI1FdaIcxauwo4BhgM9LLW3lHtLdcDvw52arln/Xp4550IS5GFhXDmmREMloESCdi0\nKZKSpOe5Pr/160MfSkREckjam7Vaa/daa9+w1tb4p2b/6x8HO7XcE1k/WHm5u32Pv3lpPjrrLBdC\nIyhJqi9MREQaIq2i2P6bdNfJWvv7xk0nt/n9YKHfL3LePHcZZj5dFVld69YVJck//jHUuwWccAK0\nb+9KzV+qfkdVERGRg0i3M+m3wGZgO2AO8h4LKIQdQioFp50Wwd2Dkkm3idXZZ4c8UIZLJGDaNJgz\nJ9Tlx6ZNYcwY9YWJiEj9pFuOXAi0Al4ELrHW9q7lo09408x+kfWDWetWf8aNc8tu+WzixMhKkp4H\nK1bAmjWhDyUiIjki3cb8k4HhwCfAdGPMcmPMlcaYLqHOLodE1g+2aBGsXp3fpUhf69auNyyCqyT9\nvjBtVSEiIumqT2P+Umvtj3Gbtl4DjAVWGWMeN8a0qOvrjTHfNcb8xxjz2f6PecaYiQ2eeZaJbH+w\nZNLVxyZNCnmgLJFIwMaNMHduqMMMGAAdOyqEiYhI+tIOYT5r7R5r7TTgVtyu+ROBwjS+9APcTb5P\nAoYCs4DHjDGD6juHbBRJP5i1rgfK8+Dww0McKItMnOg2qw25JNmkiVvlnDXL/TWIiIjUpV4hzBhT\nbIz5hTFmNXAXMAc42lr7aV1fa6193Fo701r7nrX2HWvtNcA2YESDZp5FIusHe/NNeO+9/Nyg9WDa\ntHElyWTSbd0RIs+DtWtdb5iIiEhd0gphxpgvGWNeAN4C+gPfAoqttddaa1fWd1BjTFNjzIVAG+Dl\n+n59tvH3jwo9hCWTYAxMnhzyQFkmkXBbdoRcklRfmIiI1IexadROjDHlwBrgH7itKmpV1z5hxpjj\ngXlAS9x2Fxdba2cc5L2XAZcBdOnSZchDDz1U5zwbY/v27bRp0yaUY//2t/1IpTrz+OMvhVqOHPbV\nr7KnbVsW33ZbeIPUQ5jntD6alpUxcvJkNkycyHs/+EFo41gLU6eOYPDgT/nZz5aFMkamnNNcofMZ\nPJ3TYOl8Bi+Kc+p53iJr7dC63pduCFuF2wfsUGxd21QYY5oDPYH2wPnAN4Gx1tolh/q6oUOH2oUL\nF9Y5z8ZIpVKMDWmpql8/OOYYeOKJUA7vLF/uBrntNggxaNRHmOe03qZMgfnzXb2wSb1bIdP2pS+5\nlbD1692iZNAy6pzmAJ3P4OmcBkvnM3hRnFNjTFohLN0tKooPsjfYgQ9gTBrH2b2/J2yhtfanwGLg\nv9KZQ7Zatw7efTeiUiS4sCE1JRIuGb0cbvXb89zFmMuXhzqMiIjkgEYvCRhjuhpj/gS808Dx69ze\nIptFtj9YMgnDh8MRR4Q8UJY6+2xo0SL0qyTVFyYiIulKtzG/vTHmQWPMR8aY9caYHxjnemAFbiPX\nr9VxjJuNMaP3X2F5vDHmJtxeYw829g+RyVIpaNfO3V8wNCtXwmuvaYPWQykqcjc0nzYt1Ksk+/SB\nI49UCBMRkbqluxL2a+A04F5gC/AH4AlcCXKCtXaYtfafdRyjK/AAsBx4ARi2/2tnNmTi2SKS/cH8\nUqRC2KH5Jcl580IbwhhXkiwtDX1HDBERyXLphrCJwFettT8BJuFu4v2+tbbEWvtiOgew1n7FWtvL\nWtvCWtvZWnuGtfaZBs47K0TaDzZ4sFuGkYM755xISpKeB5s3w9KloQ4jIiJZLt0Q1h23RxjW2hXA\nTtxmrXIIkewP9sEH8MorWgVLR1ERnHlm6CVJz3OPKkmKiMihpBvCmgB7Kj3fB3we/HRySyT9YI8+\n6h4VwtKTSLglyldeCW2IXr3coqRCmIiIHEqzNN9ngAeMMbv2P28J3GWMqRLErLW6a3QlkfWDHXec\n2yNM6la5JDlyZGjDeJ77q9m3L+S/fxERyVrproTdC6wHPt7/8QCwttJz/0P2++ADdxtHvzQVik2b\nYM4crYLVR9u2MH58JCXJTz+FN94IbQgREclyaa2EWWu/GvZEck0k+4M99pgLEgph9ZNIuNsXzJ8P\nI8K5f3zlvrCTTgplCBERyXLh3b8lz6VS0L49DBoU4iDJJPTtG/IgOeicc6B581CvkuzeHfr3V1+Y\niIgcnEJYSELvB9uyBWbNcqtgYdykMJe1axdZSXL2bNi7N7QhREQkiymEhcDvBwu1FPnEE+5f96lT\nQxwkh02d6m7m/eqroQ3hebBtGyxaFNoQIiKSxRTCQhBJP1gyCT17wtA6b9IutZk0CQoKQi1J+n//\nKkmKiEhtFMJCUFoacj/YZ5/Bs8/ClCkqRTZU+/bwhS+4kqS1oQzRuTMMHKgQJiIitVMIC0Ho/WAz\nZsDu3boqsrESCVizJvSS5Esvub8uERGRyhTCArZ2Lbz/fgSlyK5dQ91sNC+ce27oJUnPg88/DzXn\niYhIllIIC5jfDxbaJq2ffw4zZ8J550ET/fU1Svv2MG5cqCXJMWNcxVglSRERqU7/igcslYIOHULs\nB3v6aRfEVIoMRiIBq1fDggWhHP6ww+DEExXCRESkJoWwgPn9YKEtUk2bBocf7pZYpPEiKkm+/DLs\n3BnaECIikoUUwgIUej/Yrl3w1FMweTI0S/fe63JIHTrAGWe4EBZSSdLz3F/dvHmhHF5ERLKUQliA\nQt8f7Lnn3O6fKkUGyy9JLlwYyuH9K2VVkhQRkcoUwgIUej9YMuluuXP66SENkKfOPdetLIZUkmzb\nFoYMUQgTEZGqFMICVFoaYj/Ynj3w+OMVN5+W4Bx2WCQlyfnzYceOUA4vIiJZSCEsIGvWwIoVIZYi\nUyn45BOVIsOSSMCqVaHd6NHzXI6eOzeUw4uISBZSCAtI6P1gySS0bg3jx4c0QJ7zL3YIqSR56qnu\n8CpJioiITyEsIKH2g+3bB48+CmedBYWFIQwgHHaY67ULqSTZujUMH64QJiIiFRTCApJKua27QukH\nmzsXNm1SKTJsiQSsXAmvvRbK4T3PXYD52WehHF5ERLKMQlgAQu8HmzYNWrRwK2ESnsmT3V4SIZUk\nPc8tas6ZE8rhRUQkyyiEBSDUfrDycpg+Hc48E4qKQhhADjj8cFeSDOlekiNGuCytkqSIiIBCWCD8\nfrDjjw/h4K++CuvWqRQZlUTC3fZg8eLAD11Y6IKYQpiIiIBCWCBKS0PsB0sm3b0NzzknhINLDRGU\nJF9/HbZsCeXwIiKSRRTCGmn1atfLHUop0loXwk4/Hdq3D2EAqaFjRygpCe0qSc9zh509O/BDi4hI\nllEIa6RQ+8EWL3YJT6XIaCUS8N578MYbgR96+HBXllRJUkREFMIaKZVyW0yF0g+WTLoa57nnhnBw\nOajzzgutJNm8udu4VSFMREQUwhoptP3BrHVX6Y0ZA506BXxwOaSOHV3dMMSS5JtvwkcfBX5oERHJ\nIgphjRBqP9hbb8Hy5SpFxiWRgHffhf/8J/BDe557TKUCP7SIiGQRhbBGCLUfLJkEY1xpTKIXYkly\n6FC35ZtKkiIi+U0hrBH8frCBA0M4eDIJI0dC9+4hHFzq1KmTS9chlCSbNYPRo2HWrEAPKyIiWUYh\nrBFC6wd77z1XBlMpMl6JBLzzjmvgCpjnuWrz+vWBH1pERLKEQlgDhdoPlky6xylTQji4pO2881zC\nDqEkqb4wERFRCGsg/x/P0ELY0KHQq1cIB5e0de4cWknyxBPd/rvqCxMRyV8KYQ0UWj/YmjWwYIFK\nkZkikXB1wyVLAj1s06aulK2+MBGR/KUQ1kCh9YNNn+4eFcIyw5QpoZYkV6xwuVtERPKPQlgDrFrl\nPvy+nkBNm+a23z/66BAOLvXWubNL2yGUJP3vH5UkRUTyk0JYA4S2P9iGDfDyyzB1asAHlkZJJODt\nt2Hp0kAPO3Cg25xfIUxEJD8phDVAKgWHHw4DBgR84EcfdastKkVmlilT3Ma5AZckmzRxQX7WrFDu\njiQiIhlOIawBQusHSyahf3847riADyyN0qULnHZaaH1ha9e63jAREckvCmH15PeDBV6K3LzZ1TnP\nP9+tukhmSSRg2bLAS5LqCxMRyV8KYfUU2v5gjz8O+/apFJmp/HAc8GrYMcdA164KYSIi+UghrJ5C\n6wdLJqG4GAYPDvjAEoiuXd0NHwMOYca41TD1hYmI5B+FsHoKpR/s00/h+edVisx0iQS89Zb7CJDn\nwcaNbk9YERHJHwph9bBqlbtnZOD7gz35JOzZo1JkpgupJKm+MBGR/KQQVg+h9YMlk9CjBwwfHvCB\nJVDdusGppwYewvr2hSOPVAgTEck3CmH1kEq5zTUD3UFi+3Z45pmK2+NIZksk3BWSy5YFdki/L6y0\nFMrLAzusiIhkOP2rXw+h9IP9+9+wc6dKkdkixJLk5s2B74AhIiIZTCEsTX4/WCilyM6dXZlLMl/3\n7jBqlPrCRESk0RTC0hRKP1hZGcyYAZMnQ9OmAR5YQpVIwJIl7n6SAenVC/r0UQgTEcknCmFpKi0N\noR/s2Wdhxw6VIrON//cVwmpYKuX27BURkdynEJYGa0PqB0smoUOHEPa8kFD16BFaSfLTT+GNNwI9\nrIiIZCiFsDSsWgVr1gRcity9G554AiZNgoKCAA8skUgk4M03A91hVX1hIiL5RSEsDX4/WKALVi+8\nAFu3wtSpAR5UIhNCSbJ7d+jfXyFMRCRfKISlIZT9wZJJKCqCceMCPKhE5ogjYOTIUEqSs2fD3r2B\nHlZERDKQQlgd/H6wsWMDvK3j3r3w2GNw9tnQokVAB5XIJRLwn//AO+8EdkjPg23bYNGiwA4pIiIZ\nSiGsDqH0g82eDR9/rKsis51fSg5wNcz/PlNJUkQk90UWwowxPzXGLDDGfGaM+cgY86QxZmBU4zdU\nKPuDJZNQWAhnnhngQSVyRxwBI0YEGsI6d4aBAxXCJLvcckvN79nSUve6iBxclCthY4HbgZFACbAX\neN4Yc1iEc6i3wPvBysvh0UdhwgRo3Tqgg0psEgm3p8S77wZ2SM+Dl15yF9CKZINhw+CCCyqCWGmp\nez5sWLzzEsl0zaIayFo7vvJzY8wlwFZgFPBkVPOoD2vdD5NA+8HmzYMNG1SKzBVTp8KPfwzTpsFP\nfxrIIT0P/vhHePVV3c1KMs8nn8DKlVU/Vq2CVq3g9NOhbduRlJXBl77k3vvOO9C3r24KIlKbyEJY\nLYpwK3Gf1PZJY8xlwGUAXbp0IeXXBUOyffv2GmOsX9+StWtP4bzz3iGVWh/IOH3//Gd6FBQwt107\n9oX8Z4pbbec0Fw0+7jia3HMPi0aMCOR4TZs2w5hR3H33KvbuXV3lc/lyTqOi81lTWVkTNm5sycaN\nhWzY0JING1ruf96SDRsK2bGj6j8bbdrsoVu3nfTqtZPCwhYsX96WNm32cO+9zfj7391vrwUF5fTq\ntYPevd1HcbF77NJlV3C/4OYofY8GL5POqbHWxjOwMQ8DRwNDrbWHvFHL0KFD7cKFC0OdTyqVYmy1\nxq+774avf93dJnDAgAAGsRaKi+H44+GppwI4YGar7ZzmpN//Hq64At57z/3KH4CTToL27WHWrKqv\n5805jUg+ns/du93FRrWtZq1cCZs2VX1/YSH07l31o7i44r/bt3fv80uQEyasYubMYu67z/U4LllS\n9eODDyqO3aaN+9k6cGDFx4AB0LVrgNWHLJeP36Nhi+KcGmMWWWuH1vW+WFbCjDG/B04FTq0rgMUp\nlYJOnQLsB1u40P30+8vT1YUAACAASURBVPnPAzqgZISpU10Ie+QRuOqqQA7pefDnP8POndCyZSCH\nlDyxbx+sX3/wkLVunWtN9TVr5m4g37s3nHtuzZDVuXPdgcgPYA8/DMas4qtfLT7w/NJLq77300/h\nrbeqBrMnn4S//a3iPYcdVjOYDRzoXhfJJZGHMGPMH4ALAc9auyLq8dMVyv5gyaT7iTdpUkAHlIzQ\nsycMHx54CPv9710LoW4tKpVZCx99dPCQtXo17NlT8X5j3O1Oe/d230vVQ1aPHo3v11qwwAUu/yb0\nnueeL1hQ8/u3fXu3z/HIkVVf37QJli6tCGZLl8KDD7obi/i6dasZzo47zu17LZKNIg1hxpjbcAFs\nrLX27SjHrq+VK2Ht2sD+TXU/OZNJ9xNJv87lnkQCfvITWLEC+vRp9OFGj3Y3iy8tVQjLR1u3Hjxk\nrVoFO3ZUfX+nTi5QDRniFmYrh6yePcPfE/rKK2u+5nn1+97t3Nl9VP4aa93KXeVgtmQJ3HknlJVV\nvK+4uOaq2THHaBVZMl9kIcwY82fgEmAy8Ikxpuv+T2231m6Pah7pCnx/sDffdD1DP/lJQAeUjDJ1\nqvu7feQR+J//afTh2rWDoUO1X1iuKiurCFXVQ9bKle6qwsratnWB6uij4QtfqBqyiotdb1UuMsZt\nx3fEEVW3VSwvd+epcjBbsgSeeaZiFbBJE3e+KgezgQPhqKOgoCCeP49IdVGuhH1n/+ML1V7/OXBD\nhPNIi98PduyxAR0wmXQ/USZPDuiAklF69YKTTw4shEFFSXLHDm0pl2327HEr6QcLWRs3Vn1/y5YV\nweqUU6qGrN69oUMHNapX1qSJuwamb1/Xx+bbs8dt2Vc5nL35ptua0e+Da97crZJVvyCguNgdV3LT\nLbe4fesqr7SWlrqSeW0ruVGJcp+wrPkRElo/2OjR0KVLQAeUjJNIwH//d2AlSc+D3/wG5s51qx+S\nOcrL3XZ/BwtZa9dWbX5v2tSVBXv3hokTa4asLl0UAIJQUOB6xKpfTFVWBm+/XTWcvfwy/POfFe9p\n1cp9XeVgNnAgdO+uAJwL/A2FH37YPa98MUmc4twnLGOtWBFwP9jbb7v/82+7LaADSkaaOtWFsGnT\nAvnVatQodx1HaalCWJDS+Y3YWnd714OFrNWrYdeuqsft3t0FqtGja4asI45wf5cSj8JCGDzYfVT2\n2WfuSs3KJc2nn4a//73iPe3b176NRqdOkf4RpBJrXYXg009d6T7dR2OgpASGDh3EqlUVF5PEST8W\nahF4P1gy6R6nTAnogJKRiovdv+6PPBJICGvTxl10qb6wYPm/Ed99N2za1JqXXoKbb4bx411pyw9a\n26t1qh5+uAtUJ5zgugoqB61evdQEno3atnXl31NOqfr6xx9XDWZLlrh/sP/yl4r3+Pd5rR7O2raN\n9s+QrfbudeEo3QBV/bW9ew99/KIiV8bv0MEF6b593YUry5bB/PmHce218QcwUAirVSrl/gcLtB/s\nlFPcr8OS2xIJF8BWrnT/OjeS58FNN7nf2PXDPT07d7pS4bp1br+s2h63bfN3iqm4ueGzz1aEqpKS\nmqtZ2gYhfxx+OJx2mvvwWet6+apvPvu3v1W9WvXII2sGs2OPdeXOXNLQ1Sj/sfovOdUVFFQEqA4d\n3KYCfftWPD/UY7t2ta88+yXISy5ZxR13FNf7Ct4wKIRVE3g/2IoV8Prr8L//G8DBJONNnepC2LRp\nrjTZSJ4Hv/wlzJnjeonyWXm520vKD1IHC1kff1zza1u2dPthde/urjqdNMk1bD/3nLsrxs03u394\n1fsjB2OM26esWzcYN67i9fJytwd39XA2a1ZFydoYFyCqb6PRr5+7UKC6qJrIq69G1TdMpbsa5Qek\nPn2qPj9UmCosDPb/x0NtKBxnEFMIq2bFCndbjcBKkdOnu0fdsDs/9O7t/pV/5JFAQtiIEe6HdGlp\n7oYwa91K38FWrfzHjRtr/tBv0sQ1tfubkZ56qgtaPXpUhK4ePdwP9so/0EtL4b773G/Ejz9ezMUX\nx/8bsWSnJk3cqmlxMZx9dsXre/fC++9XDWZLl7q7A+zbf5+YZs2gf/+a4eykkyoHhoM3kWfjalRc\n6rOhcJQy6BRlhlD6wQYPDqQ0JVkikXDbVKxa5X4yN0JhodtZPFv7wnbtqrs0uH59zc1Hwf0g94PU\nscdWBKrKj1261P8Hfab+Riy5xQ9Y/ftX/R181y5YvrxqMFuwAP71r4r3tGzpypoTJsBRRw3m/fdd\nR8vNN7sfLUGuRh1qVSro1ag4BbGhcBgUwqrx+8GOOSaAg33wAbzyiqsnSf6YOtX9pJw2LZDNeT0P\nbrgBtmxp/NSCUl7ubp1T1+rV5s01v7ZFi4ogNXiwWz2ovnrVvXt4PTSZ+hux5IcWLWDQIPdR2fbt\nrmm88jYaGzfC0qXtaNXK/T+V7atRUpP+eioJvB/s0Ufdo0qR+aVPH1dPeOSRwELY9dfD7NnuB2vY\n0ikNbthQ8zdwYypKgz17ulJqbatXhx0W72/XmfobseS3Nm1cH9iw/deK+Cu2kyevYubMYu68U9+j\nuUghrJL33w+4HyyZdLv/BbKsJlklkYCf/tRtKNWrV6MONWdORV/Yeee51xrSpLt7twtPdQWs2vpE\n2rWrCFKeVzVY+f/dtat+6xYJgkrm+UM/MisJtB/sww/d0sXPfhbAwSTr+CFs2jS44opGHWrECLdK\n++STLoRVb9ItL3dlv7rC1Ucf1Tx28+YVYWrQINeDUtvqlW6bJBIdlczzh0JYJYH2gz32mPuXc+rU\nAA4mWadvX9fw9MgjjQ5hngdf/rLbj+i//usE3n7b3abymmsqSoP+TYt9xrjv5e7d3fZ0J59c++qV\ntmUQyTwqmecPhbD9Au8HSybhqKPg+OMDOJhkpUQCrr7abSLUs2ejDvW977kQtnhxB5o3d/tl9egB\nY8bUXLXq0cOVBgsKAvpziIhIKBTC9lu/vpB16wL6TWPLFlczuuIKLTPkMz+ETZsGP/5/7d15lFTV\nuffx78OgKIgDiAMqgwwyOQFxuEYBYyAxeaOExhglicYh8ZpJDV7jTVaG9SY3qFETEzKHRK8xSDsF\npzjQEgcMaBQZHLFFUaRRBBobQdj3j2cXdbq6m56q61R3/T5r1eo+Q53atc+pU0/tZ+9zLmnVptat\n81arU05ZyYMPHsKvfqVfxSIi7V2ntAtQLJ55xoed5aU/2F13+dAxjYosbYMGwZFHekqyFTJ9wG69\nFS68cAWzZ/t0e712mIiIuJIOwmbMyH6RPfPMXuy3n3dinjGjlRsuL/f005gxrS6jtHNlZX6tuNdf\nb/Emkp10oXYnXRERab9KOggbO9ZbFB5+2IOwYcPgjDOy12lpkQ0b/E7AkycrFSkehIGnJFto+vS6\nqcfx4/N7DzkRESm8kg7CMi0KU6bA2rW7smhRHq7DcvfdfkEmpSIFYPBgOOKIVqckRUSk4ynpIAw8\n4Jowwf+fNi0PnZ3nzPGhaccf3+qySQdRVgZPPNGqlKSIiHQ8JR+EzZsHjzwCU6e+zq23trKz86ZN\ncO+9norsVPJVKxmZlGR5ebrlEBGRolLSkULyyuNf/eorrR91dt99UFOjVKTUNmSIX45eKUkREUko\n6SAs76POysv9Yk4nnpi3MkoHUVYGjz/uNycVERGhxIOwvI46++ADmDsXTjtNdzGWupSSFBGRHCUd\nhOXVAw/Axo1KRUr9hg71W1gpJSkiIpGCsHwpL4c994STT067JFKsysrgscf8rtsiIlLyFITlw9at\ncOed8OlPwy67pF0aKVZKSYqISIKCsHyYN8/vsKxUpOzMYYfByJFKSYqICKAgLD/Ky6F7d5g4Me2S\nSLHLpCTffDPtkoiISMoUhLXWtm1wxx1w6qmw225pl0aKXVkZhAC33ZZ2SUREJGUKwlrr0UdhzRql\nIqVphg2DESOUkhQREQVhrVZeDt26wSc/mXZJpL0oK4N//hPeeivtkoiISIoUhLXG9u2eVpo4EXr0\nSLs00l4oJSkiIigIa51//cuv+aRUpDTH8OH+UEpSRKSkKQhrjfJy6NrVrw8m0hxlZTB/PqxenXZJ\nREQkJQrCWioEmDPHr5C/115pl0baG6UkRURKnoKwlvr3v6GyUqlIaZkRI3ykpFKSIiIlS0FYS5WX\nQ+fOcNppaZdE2qtMSvLtt9MuiYiIpEBBWEuE4EHYSSdB795pl0baq7Ky7AhbEREpOQrCWmLZMnjh\nBaUipXVGjPD7SSolKSJSkhSEtUR5OZjB6aenXRJpz8y8NeyRR/yuCyIiUlIUhLVEeTkcfzwccEDa\nJZH2TilJEZGSpSCsuV5+GRYvVipS8mPkSBg6VClJEZESpCCsucrL/e/kyemWQzqGTEqyokIpSRGR\nEqMgrLnmzIExY6Bfv7RLIh3FlCmekrz99rRLIiIiBaQgrDleew0WLfIvTZF8OfxwGDxYKUkRkRKj\nIKw5Mp2n1R9M8imTkpw3D6qq0i6NiIgUiIKw5igv91aLQYPSLol0NJlRkkpJioiUDAVhTfXWW/D4\n42oFk7ZxxBEe3CslKSJSMhSENdXtt/vtihSESVtIpiTXrk27NCIiUgAKwpqqvNyv5zR8eNolkY6q\nrAy2bVNKUkSkRCgIa4qqKr+1zGc/6y0WIm3hyCPh0EOVkhQRKREKwprizju9hUKpSGlLmZTkww8r\nJSnty4wZnkpPmjfP54sUgyI9RhWENUV5OQwYAEcdlXZJpKPLpCTvuCPtkog03dixMHUqzJ6Nbd3q\nX25Tp/p8kWKQOUYzgViRHKNdUn319uC99+Chh+Ab31AqUtreUUfBwIGekjzvvLRLI9Kw9eth4UJY\nsMAfW7bAGWdwohl06gTjxsGTT0JNDYwaBQcdpHOopOekk+Daa+G00xg0fjw89hjMng3jx6daLAVh\njfn732HrVqUipTAyKcmrr4Z33oFevdIukYi3zi5fng24FiyAZct8xDj4gKUpU2DVKuz++/2HxMsv\n+w/YjL328mDs8MP9MWqU38B+jz3SeU/SMW3f7ne3Wbq09mP5cv9BABx0553w3e+mHoCBgrDGlZdD\n377wkY+kXRIpFWVl8NOfekryy19OuzRSiqqqvBUrE3D961+wcaMv22cfOPZYOOMM/zt2rAdYMb1T\nOW0a/e+911sZjj4aliyBxYv98dxz8Je/ZLcF3tUjE5Rl/g4eDJ07p/PepX0IAV5/PRtkLVmSDbY2\nbcqu17evB/vjxvkx9bvfsXLSJA6ZOdODMLWEFbHqarj/fjj/fG9eFymEo4/2L6Zbb1UQJm1vyxYP\nkJKtXK+84ss6d/YLCU+b5gHXscf6RYVz04qZ/jWzZ1NpRv9zztkxzfjx8B//kV03BFi5MhuUZQK0\nuXO9xQ2gWzcYMaJ2YHb44dCnT2HqRIpHCLBqVd2WrWXLagfzBxzgx8x55/nfESO8hXavvXx55hi9\n7TZWmHHIBRfUPkZToiBsZ+65BzZvVipSCiuTkvzZz+Ddd73lQSRf3nijdsD11FN+ngP/IjvuOLjw\nQg+4Ro+G3XdvfJsLF2a/zCoq/O/s2T4/9wvODPr188enP52dv3mzt2JkArPnnoP77oNZs7Lr9OlT\nO515+OH+RdutW2trRdIWAqxeXTvQWrLEg63167Pr9enjAdYXv+h/R470Y6Cx82RzjtECUhC2M+Xl\nvsNPOCHtkkipKSvzodN33AHnnpt2aaS9qqnxICsZdK1a5ct23dWDrIsuyrZytbTz/PTpdec1N9XT\nrZsPTMkdhV5VVTswW7wYZs7c0b+HTp1gyJC6rWb9+imDUYxCgDVr6rZsLV0K69Zl1+vVywOss87K\ntmyNGAG9e7fsdfNxjLYBBWENqamBu+/2A0B9E6TQRo+G/v09JakgTJoiBE8jJgOuZ5+FDz/05QMH\n+gixTMB1xBGwyy7plrkp9t0XJkzwR8a2bf5ek4HZ00/XvtDxHnv4l3huf7NMekra3tq1dftsLV3q\ng44y9t7bg6upU2sHW336lMRoWgVhDbn/fu/cN2VK2iWRUpRJSV57rVKSUr/cS0QsWJD9cuvRwwcT\nTZ/uAdcxx3Ss/lSdO3vr15Ahtc/R1dX+JZ8cCDB7NvzmN9l1Dj647ijNoUOha9fCv4+O4t1362/Z\nWrMmu07Pnh5cTZ5cO9jaf/+SCLYaUtAgzMxOBC4DRgMHAueEEGYVsgxNVl7uEfq4cWmXREpVWRlc\ndZXfseGcc9IujaSpKZeI+Mxnsq1cw4eXZgt+jx4ecB5zTHZeCPDmm7VbzZ57Dh54wC8/BB6ADRtW\nt9XswANLOkCoY/36+lu2Vq/OrtOjhwdXn/pU7WCrb1/VZT0K3RLWA1gC/CU+itOWLX59sNNP168j\nSc+YMdmUpIKw0tKSS0RI/cw8AOjbFz7xiez8LVvghRdqB2YVFXDTTdl19tmnbmA2ciR0717wt1FQ\nGzZ4kJ/bspXpTwg+YGP4cJg4MRtojRzpLY0KtpqsoEFYCOEe4B4AM5tVyNduloce8ohfoyIlTWae\narn+eu+wuvfeaZdI2kI+LhEhzbfLLh5UjRoFn/98dv66dXUHAvzpT57qBK/7gQPrjtIcOLD9tT5W\nV3sLa27r1uuvZ9fp1s2DrQkTardsaeBDXljINGcX+oXNqoGLG0pHmtkFwAUA++233+hbbrmlTctT\nXV1Njx49ABh61VXsW1HBY7ffTmgPHVeLVLJOpWX2WL6c0RddxPOXX87qSZNUp3mWRn3uWlVFz2XL\ndjx6vPginbdsAeCDXr3YMHz4jsfGIUPY3s4uv9Ahj9Ht2+m2ejXdV6ygx4oVO/7utmoVtn07ANt2\n3ZVNAwawacAAqgcOZNPAgWw69FC27rlnq146H/XZafNmdn/tNbpXVu547F5ZyW6JNOL2rl15/5BD\n2NS//47H+wMGULP//u0vuGxEIY7R8ePHPxVCGNPYekUbhCWNGTMmLFq0qE3LU1FRwbhx43wk0f77\nw8c/Djff3Kav2dHtqFNpuRD8wq0jR8LcuarTPGvz+nz/fR+1t7NLRGRauFpziYgiUlLHaE2Np+1y\nLzxbVZVd54AD6l4+Y9gw3/9N0Kz63LwZnn++bp+tV1/N9h/s2tUHImTSh5mWrYEDoUtpjNUrxDFq\nZk0Kwkqjxptj/nwfYaRUpBSDTEry5z/3m8lL8eqol4iQhu22mwfSo0fXnv/223UDs1/8Aj74wJd3\n7uyBUO4ozUMO8c/8jBne1y95Dat583w07PTpvp0XXqjbZ+uVV/zeieAB1ZAhXrYvfCEbbA0apL7O\nRURBWK45c/yDNWlS2iURcZs3+yiuu+7ykzTUPiFLOtav9w7zyQ70pXKJCNm5/faDU07xR8aHH8JL\nL9Xub/bkk/C3v2XX6dnTg7FeveBHP4If/5juPXrAww/DNdd4QPXHP/rN0TO3eOrc2QOrUaPgc5/L\ntnANHqwgvx1QEJa0fTvcfruPoOnoo1+k/Zg82a8QPnMm/OQnte7TJwWSvETEE0/43+XLdYkIabou\nXTwNOWyYf34zNmzI3uQ8E6A98oh3mv/61xmb3Mbq1R5kTZmSbdkaOrTJqU0pPoW+TlgPYFCc7AQc\nYmZHAu+GEFYWsiz1evxxP8h1gVYpJhMmeCA2Zw4jv/MdP2FfcomfvO+5x3/tdu2afTQ2nXm0875H\nLdZYqgf8IpPJFq6FC+teIuJzn9MlIqT1evaE44/3R0YIPkLx8svhllvg/PN9lPRuu6VXTmkThW4J\nGwPMS0z/ID7+DHypwGWpq7zcv7BOPTXtkojUdsUVMGcOvZ94wqd/8IPWb7NLl6YHbvmezsc2unRp\nWSA5dmy2JdEM/vEPv+bW2Wf7bcoWLIAVK3xdXSJC0mDm/bsefJDKadPof/vtcOaZqd/nUPKv0NcJ\nqwCK8+wVAtx2m4+K7Nkz7dKI1LZ+PfTqxRsnnshBFRV+Jf2jjvK+Ylu3+rWmMv+31XRNjbe+NXX9\nQmhpEDdkCEyaxLF77pkdyXbDDT6S7bjj4Ctf8YBr9Gi/KKVIISW6HFSa0f+cc7I/HBSIdSjqExbt\n8cILsHJlfloYRPIpc0K+9VZeNuOgr32t+E/IIXg/qkIGiU2Z3rQpO92zJ92qqrzT/KWXdphLREgH\nsHBh9vNdUeF/Z8/2+cX6mZcWKe0gLNE3ZN/58z29sffePl+jzqRYtMcTspl/nrp0Kc5+LDGwrZw2\njf733gu9e/vtVkSKQX3fP+PHF+/nXVqstIOwTN+Qv/2N3vPne9+P887TqDMpLjoh55dSPSJSJEr7\nxk+ZFoUpU9h91Sq/+J1OxCIdW7JlEWq3LIqIFFBpt4SBn4BPPRVuusmHASsAE+nY1LIoIkWitFvC\nwFMT991H5bRpcOONPi0iIiLSxko7CEv2DTn3XE9JTJ2qQExERETaXGkHYeobIiIiIikp7T5h6hsi\nIiIiKSntljARERGRlCgIExEREUmBgjARERGRFCgIExEREUmBgjARERGRFCgIExEREUmBgjARERGR\nFCgIExEREUmBgjARERGRFCgIExEREUmBgjARERGRFCgIExEREUmBgjARERGRFCgIExEREUmBgjAR\nERGRFFgIIe0yNMrMqoDX2vhlegNr2/g1So3qNP9Up/ml+sw/1Wl+qT7zrxB12i+EsG9jK7WLIKwQ\nzGxRCGFM2uXoSFSn+ac6zS/VZ/6pTvNL9Zl/xVSnSkeKiIiIpEBBmIiIiEgKFIRl/TbtAnRAqtP8\nU53ml+oz/1Sn+aX6zL+iqVP1CRMRERFJgVrCRERERFKgIExEREQkBQrCmsnMLjOzyrTLIe2TmfU2\ns2Bm49IuS0PMbIqZFX0/hXzVpZndYGYVzXxOMLMprXndYtYejtOOzszGxH3QP+2ySNtRECYi0gpm\n1j9+WRbFdYdEpP1QEFbCzGyXtMuQYWZd0y5DR1JM+1ZEROrX4YMwMzvRzBaYWbWZrTezJ81sZFx2\nrpmtNLP3zezvZnZRbhrGzKab2er4/L8APVJ5I3lgZhVmNtPMro63gnos/oL/qpndGevhRTMbb2YH\nmdn9ZrbJzJ4xs6MT29nTzG40szVmttnMVpjZNxPLg5ldbGZ3x22+ZmZnJ5ZnWg7ONLOHzawGuDAu\nm2xmz5nZB2b2upldaWaWeG6lmX3fzG6K+2S1mV1WmBqsy8wmmdk/zWydmb0b62xYYvlYM3sq1tO/\ngWNynt/ZzP5gZq+aWY2ZvRSPuU6JdbqY2bXxNdbF/2cmU2j17ds4/xIzWxz34yoz+72Z7ZVThi/E\nffS+mc0F9mub2tq5AtVl51hHmbq8Duicsx2Lz3slbue55PFbj1fj34XxuK5IlPcfZrbWzDaY2aNm\ndlwrq6lFClS3s8xsrpldHj+X683sf8ysU/zMronzL8/Z9k6P0fi6S81st0RZHo3HalGIn79fmdmP\n4/5eE4+zTnH52Wa20Mw2xmW3mlnfnG1MMrPn4z74JzAkZ3kvM/urmb0R98FSMzunnnLMNLNr4n6u\nMrNvmNmuZvZLM3vP/DtvWptXijRNCKHDPoAuwDrgauBQ4DDg88Aw4DhgO3A5frCfD1R5lex4/lRg\nCx4gDAGuBDYAlWm/txbWRwWwEbgm1sUwIACrgDOBwcDNwGrgfuAz8X3fAyxObOcXwDPAR4D+wDig\nLLE8AO/k1Nt2YExc3j+uUwlMAQYABwGjgW3AD+LzzgKqga8ltl0Z98GVcZ0L4z6anFKdfjY+BgOH\nA7OBl4FdgO7AGuBWYCQwEVge3/u4+PyuwA+BsbFepgLvAV9OvMZ/xeP4s8BQ4HpgPVCxs30b538T\nmBC3fRKwGLgx8bxj4r5J1uc7yc9BB6vL6bHupsZ6+kU8npJ1+f+BF4BJ8dj8PLAJODXnGJ8S/x8b\npycC+wP7xPkTgGn45+ww4Ia4H3t30LqdFevy1/H9nhmPrfuAn8Tj6ytxu6MTz2vsGO0OvAj8Mk5/\nDz9H9UnjM99A/VbE4+qH8X1OBT4EzozLzwU+CQzEz5vzgPmJ5x8MbI7H42Hx+W/Euuof1+kLfBs4\nMm7nAvzcd3JOOTYA34/7+tK4jXuBbwCDgB8BHwAHpl1veoQOH4TtEw/Ak+pZ9lfgvpx5v6V2EPY4\n8LucdR6kfQdhi3PmBeAniemRcd4liXnj4rzecfou4E87eZ3QQL3dFP/vH9e5NGed/wUezpn3feCN\nxHQl8EDOOr8HHk27fmNZuuOB5AnxJPke0COx/GwSX24NbON/gAcT028B/5WYNuB56gZhi5tQvknx\nBNwpTt/cQH2GDlqXbwJXJqY74V/wFYnXrAE+mrOd64B7co7xTBCWOZ7HNPJ+LO7Lszto3c4CXgc6\nJ+Ytyj0u42f4sqYeo3HeGDzg+CGwFfhE2nWYU+YK4ImceQ8Av29g/cNi/R4Up38cj0NLrPPfJIKw\nBrZzS/I1cssRj7kq4K7EvK6xLqekXW96hI6djgwhvIufGO43T41dYmYHx8WHAf/KecqTOdPDgCdy\n5uVOtzdP1TNvceL/t+Pf5+qZ1yf+nQlMNbNnY5P7SfVss756G54zb1HO9DBiGi3hUaCvmfVs5rYL\nwswONbObY+pqA15XnYBD8PezOIRQnXhKnePHzL5iZoti6qAa+FZ8Pma2J966suNYDX4mXVhPcers\nWzObYGYPxBTGRuA2vPVj/7hK0RzjBarLA5LPCyFsp/bnfjjQDbjPPN1dHbfzVbw1vTnvp4+Z/cY8\nxb8eb6nskylPIbV13SYsCyFsS0y/Te1zSWZe5lzSlGOUEMIivIXyu8BvQwj3NqsCCmNxzvSbxPdp\nZkebd/l4Lb7HzLkvU3/DgAXxs51Rax/ENOyVMXX7TtwHk6m7D3aUI25vDYl9EELYirfI9kFS16GD\nMIAQwjl4ymU+8P+AF81sIv4LoeiH4beBTfXM25r4P+xkXieAeALsh6d5ewN3m9mf8lCWne2TYt1X\nfwf2xdN4xwBHVSLRqQAACJFJREFU4WmIXfD3s1NmdgbeyjILTwMdCfwqPj+pKe+/Vn2aWT/gbjy1\nVIane8+NizPbb7SMBVSoutyZzDnx0/H5mccI4OPN2A7An/H03beA4+N23mhmefKlUHW7NWc6NDAv\n01eqKccoZmZ4q9024NA4XWzqfZ9m1h3v3vE+np4ei7f2QfM+h5fh6cWrgJPxfXAHrdwHkq6S2Akh\nhGdDCD8NIYzDm2u/iH/oP5Kzau70cuDYnHm50yUphLA2hHBjCOFLwJeBL5rZrolV6qu35Y1sdhl+\nok06AU9HbmzltvPOzHrhv2B/HEJ4MISwHNgD74sI/n5GxZNwRm7ZTwCeDCHcEEJ4OoTwMokWlxDC\nerz/y45jM34BjW1CEcfgJ+hvhRCeCCG8CByYs86yespU8GO8gHX5VvJ5sS6Tn/tleCqsXwjh5ZzH\naw0Uf0v82zln/gnAL0IId4cQluItYQfsrB7aQiHqthWacowCXAIcDZwYy/a1PLx2oRyG/1j9Tghh\nfgjheeq2Qi0DjskJLuvbB3+P591ngFfI6bwv7U+HDsLMbID56JzjzayfmY3HO6UuA34OfNzMvm1m\ng83sy8DpOZu4Hg8uzo/rXEHOqKFSZGY/NLPTYp0Mw5vEV4QQPkisNjmn3k7Gf0nvzDXASeYjqYaY\n2Vn4L78ZOesda2ZXxG2fD3wBuDY/765Z1gFrgfPNbFBMy/4ab2EA72/1IfBHMxthZqfgHeCTXgSO\nNrNPxPfzXbxzctL1wHQzO93MhuL1dACNt469hH/Gvxk/C2finaCTfg58LKc+cz8HhVDoupwS6/I6\nEoFRDPavBq42Hz09yMyOjKm4Cxoo+xq8H9lEM9svpj0z5TnbzIab2Vi8/86WBrbRlgpVty3R6DFq\nZkfgqcgLQgiP46nhn1oc5d4OrMQD+4vNbKCZnYp3jk/6Nd638DozG2p+IeCv5KzzInCymZ1gZpmB\nHgPatujS5tLulNaWD3yo/W346L8P8A/DDKBrXH4u3pG0Bm+uvxSoydnGFfhJtho/WX2f9t0x/4ac\neTs6GMfp3uR0yCXbiXRknL4SWIo3r7+Lj54clrPNi/FRUTWx3r+YWN6fBjoy4wHdc/iX1evxtZKd\nVSvjPvhr3CdvA5enWKcTgCX4yKYleKqmGvhSXH4M8HQ8/p7F01w76hdvBfgD/kX5Xvz/e8ljDG+x\nuC4uXwf8DE8L3buzfRvnfz0e/zXAQ/ioq1qdfYFz4j6qwUdRXUw6oyMLVZfXxuXv4aPRZlJ7kIPh\nLS2ZVrEqvJP1KTv53JwX63Ab2U7+R+D9zWrwVotp8X19v4PW7Sxgbs7rzgVm5cxbAFzdlGMU75+3\nBPhjzjZujOXcNa3Pfk556nz+kvUBnBGPgc14/86J1D3PnoqPyt2M9409i8RnFdgb/z7biH8nzcBT\nwhWNlKPOMYe3rl+cdr3pEfzLTZyZXQt8LIQwKu2ytGfm11orCyHMaYNtV+Inmavzve32xMyeBh4L\nIbSntIyIiCR0aXyVjsvMvo3/wq0GPoY3/34n1UKJ5IidlycCj+Cf2QvwVpaG0mMiItIOlHQQhncK\nvQzYE7/q9RV4nxGRYrId7/d2Fd5/Zhl+naTcS3yIiEg7onSkiIiISAo69OhIERERkWKlIExEREQk\nBQrCRERERFKgIExEOiQzG2dmwcx6t3I7s8xsbr7KJSKSoSBMRFJjZn3N7Lfx5s1bzGyVmf3OzA5q\n5nYqzeyynNmP41fDf6eVxfwGcHYrtyEiUoeCMBFJhZkNABYBI/H7uQ7Cg50RwEIz69+a7YcQtoQQ\nVodWDgEPIawPIbzXmm3sjJmlcUNvESkCCsJEJC2/xK+B9rEQwkMhhJUhhHn4hZO3x+WYWYWZ/drM\nrjezdfFxlZl1yiwH+gFXxfRjiPNrpSPN7EtmVh3vf/i8mb1vZneZ2Z7xXpIvmdl6M7vRzHbLFDKZ\njkxsM/dRkVj/eDN7JG5/lZnNNLOeieUVcd7VZlaF36JGREqQgjARKTgz2weYBPwyhPB+clmc/hXw\nCTPbO84+Cz9fHQdciN8tIHOj58nAG8AP8fTjATRsV/wesWfhN5UfA8zBW+I+C5wGfAq4qIHnZ1Kc\nmccY/F6KFfF9jQL+AdyF39VgMnAk8Mec7ZyN36Pyo/iFeEWkBJX6FfNFJB2D8SBkeQPLl8Xlg+P0\nW8DXY2rxeTMbAlwC/CyE8K6ZbQM2hhBWN/K6XYD/DCG8AGBmNwPfAvYLIayN8+4ExgPX5D45hLAF\nv/kxsbVsLjAP+EFc5dvA30IIO55rZl8F/m1mfUIIa+LsV0MIlzZSVhHp4NQSJiJpaqi/luUsX5DT\nt+sJoG8yzddEH2QCsOhtYHUmAEvM67OzjZiZAbOAzsC0RNlGA2fHtGe1mVWTTTcemtjEU80st4h0\nQGoJE5E0vIQHWCOAO+pZPiwufyXPr/thznQAttYzr7EfqN8DTgTGhhA2JeZ3An4PXFvPc1Yl/t9U\nz3IRKTEKwkSk4GIK8X7gIjO7NtkvzMx2B/4TuDeuB3CMmVmixelY4M0QwoY4vQVvlWpzZjYFmA6M\nDyG8kbP4aWBECOHlQpRFRNo3pSNFJC0X4z8EHzSzCWZ2sJmNAx7A05EXJ9Y9ELjOzIbGIOjb1G5t\nqgQ+Gq871qqLs+6MmY0E/gx8B1hpZvvHxz5xlZ8CH4mjOY8ys0Fm9ikz+01blUlE2i8FYSKSihDC\nK/jowqXAjcAK4Ga8s/7YEMKridX/F2/pehL4HfAHagdh3wMOxtOXVW1Y7DHA7sB1+GCBzOM2gBDC\nYjxN2R94BHgW+Anez0xEpBZr5XUMRUTaVLwG15IQwsWNrSsi0p6oJUxEREQkBQrCRERERFKgdKSI\niIhICtQSJiIiIpICBWEiIiIiKVAQJiIiIpICBWEiIiIiKVAQJiIiIpKC/wOM+BjWEbE6pgAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot RMSE \n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "ax = error_rate.plot(x='optimizer', y='rmse', style='bx-', grid=True)\n",
    "ax = error_rate.plot(x='optimizer', y='mape_pct', style='rx-', grid=True, ax=ax)\n",
    "ax.set_xticklabels(param_list)\n",
    "ax.set_xlabel(\"Optimizer\")\n",
    "ax.set_ylabel(\"RMSE/MAPE(%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min RMSE = 1.650\n",
      "min MAPE = 0.835%\n",
      "optimum optimizer = adam\n"
     ]
    }
   ],
   "source": [
    "# Get optimum value for param and param2\n",
    "temp = error_rate[error_rate['rmse'] == error_rate['rmse'].min()]\n",
    "optimizer_opt = temp[param_label].values[0]\n",
    "print(\"min RMSE = %0.3f\" % error_rate['rmse'].min())\n",
    "print(\"min MAPE = %0.3f%%\" % error_rate['mape_pct'].min())\n",
    "print(\"optimum \" + param_label + \" = \" + str(optimizer_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuned params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param</th>\n",
       "      <th>original</th>\n",
       "      <th>after_tuning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lstm_units</td>\n",
       "      <td>50</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dropout_prob</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>optimizer</td>\n",
       "      <td>adam</td>\n",
       "      <td>adam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epochs</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>batch_size</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rmse</td>\n",
       "      <td>2.18517</td>\n",
       "      <td>1.64993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mape_pct</td>\n",
       "      <td>1.06261</td>\n",
       "      <td>0.835126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          param original after_tuning\n",
       "0             N        9            5\n",
       "1    lstm_units       50          128\n",
       "2  dropout_prob        1            1\n",
       "3     optimizer     adam         adam\n",
       "4        epochs        1           50\n",
       "5    batch_size        1           16\n",
       "6          rmse  2.18517      1.64993\n",
       "7      mape_pct  1.06261     0.835126"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'param': ['N', 'lstm_units', 'dropout_prob', 'optimizer', 'epochs', 'batch_size', 'rmse', 'mape_pct'],\n",
    "     'original': [N, lstm_units, dropout_prob, optimizer, epochs, batch_size, rmse_bef_tuning, mape_pct_bef_tuning],\n",
    "     'after_tuning': [N_opt, lstm_units_opt, dropout_prob_opt, optimizer_opt, epochs_opt, batch_size_opt, error_rate['rmse'].min(), error_rate['mape_pct'].min()]}\n",
    "tuned_params = pd.DataFrame(d)\n",
    "tuned_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on test set = 2.988\n",
      "MAPE on test set = 1.886%\n"
     ]
    }
   ],
   "source": [
    "# Split train_cv into x and y\n",
    "x_train_cv_scaled, y_train_cv_scaled = get_x_y(train_cv_scaled_final, N_opt, N_opt)\n",
    "\n",
    "# Split test into x and y\n",
    "x_test_scaled, y_test_scaled = get_x_y(test_scaled, N_opt, num_train+num_cv)\n",
    "\n",
    "# Train, predict and eval model\n",
    "rmse, mape, est = train_pred_eval_model(x_train_cv_scaled, \\\n",
    "                                        y_train_cv_scaled, \\\n",
    "                                        x_test_scaled, \\\n",
    "                                        y_test_scaled, \\\n",
    "                                        scaler_final, \\\n",
    "                                        lstm_units=lstm_units_opt, \\\n",
    "                                        dropout_prob=dropout_prob_opt, \\\n",
    "                                        optimizer=optimizer_opt, \\\n",
    "                                        epochs=epochs_opt, \\\n",
    "                                        batch_size=batch_size_opt)\n",
    "\n",
    "# Calculate RMSE\n",
    "print(\"RMSE on test set = %0.3f\" % rmse)\n",
    "\n",
    "# Calculate MAPE\n",
    "print(\"MAPE on test set = %0.3f%%\" % mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'USD')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DD7AsluRqZTRNQ9MseDyORn+O212K0RiO2dzZ/1nB5HjJcZanLWdCyoRay6RE\n6wtD0k6mVXs2xlYpM/fjj/pq1UmT4M47m6O5p81sNLM7DhwGGJYNZyWdxYSUCbyy7hVcHle18r6M\nXd9OfYG2sQhCgjkhhBAdQn7+Uk6e/J70dP24w5CQmocZDQbLaQ2zejylGAw2LJYkyssz6n+ghX24\n7UMcbgfThk+rtYxv8+DDJw8HXE87mUZKlHcFcEkJXHcdpKbCggX6CtYgpJTCGQI7EmB4th7c3XPm\nPRwuOMzq9Opbe2Xa9QB8RBf9rFjJzFWhadq5mqZ9rmlahqZpStO0qXWUfdNbZkaV6xZN017RNC1P\n07Rib33daqtHCCGEAPxZsrKyNEDzn8ValcFgbvAwq92+gdzcTwKu6cOsNiyWrkGZmZu7eS4jOo9g\nWOdhtZbpHtkdCMzM2cvtHCs5VrHH3NatcOwYPPccxMTUUEtwcHr0eX+eIYOZUBCN0WBkcIJ+Pmx6\nYXq18pn2TEJNoZKZq0M4sB24H6h1RqGmaTcAZwI1/Sl4GbgeuAUYD0QCX2iaVn1JjhBCCOHlcGRh\nNutnjhqNkWhVV2R66cOsDQvmDh/+M/v23R9wzZeZM5u7Bt2cuS3ZW9iUval6Vq6kBKZOhcGDYcIE\nLDl5dI3oyuGCiszcgfwDAPSK9QZz27bp34cPb4GWN54vmFN9+2DLOwmlpXSJ6AJAlj2rWvnMoky6\nRnQlISwBgNzi4FyRXFmLBnNKqa+UUn9USn0KeGoqo2laCjAHuBVwVrkXBUwHHlZKLVFKbQSmAEOB\nC5q18YLZs2eTmprqfz9z5kwGDx58WnXOmzeP8PDw02yZEELUr7w8k6io8SQm/gqbrfYTDAyGhgdz\nxcU7cLvtAdfcbt8wa1dcrhO43cGzGvK/O/9LiCGEW4fcWnFRKbjxRv0M1S5d9Hlw06bRM6w7n+/5\nnJNlJ1FK8daGtwACM3MREZCSUsMnBQ/fvDhnjHdvvBMniLJEYQ2x+odUAXbn7ea7A9+x7/g+ukV2\nwxpiJcoSRU5xDvZyO3klea3R/AYJqjlzmqaFAB8BzyqldtVQ5AzABHznu6CUSgd2AWe3SCOF34wZ\nM1i5svpqsNpomlbtmLGbb76ZgwcPNnXThBCiGocjG7O5M/36zWXEiJ9qLdfQOXNudxmlpfvxeEpQ\nyu2/XpGZS/J+bvXsT0tYvGcx//zlnwHXsuxZJIQl0Cm00hYi+/fDV1/Bo4/CkiXw7LOwZAlX7HJz\nvPQ4tyy4hZfXvMzr618HKmXmtm6FIUNA01qqS43i217FFe0dVv/b39CuvpqLj8eSVVTxsxnw2gAu\n/vfFbMjawCW9LgHg6owILvs7DCXGAAAgAElEQVTH1/R7tR/xz8e3eNsbKtjOZp0FHFdK/bOW+50B\nN1A1PM7x3qtG07Q7gTsBEhMTWbFiRY0VR0VFYbfba7zX0txud7O1xeFwYDabG/VseXk5SqmAtpnN\n5lNqa2lpabX+2Wy2Vv+1Lysrq/X3RmMUFRU1aX3BqL33sb33D9p/HwP75wIKyciwk5HxYz1POikp\nyWzAr81+fINMK1d+jT6TCPR/oszk5+vneq5dOwqYC0RVr+I01Pfzu2qlvvnvmh1rmJY6jVJ3Kd/t\n+Q6TZgp4rsuXX9IPWDdgACUrVsDo0ZwdFcUlC7fx11vhm/3fsOzAMn/5jas3glKcs3Ejueedx75m\n/D3UFL9Hs3L0gO1A4UnGA7z6KgALNZimbWZFp8D6NTR6FPVg1Xff8Zf5OSTlOzEMAKJo8j8vTfZn\nUCnVKl9AETC10vsJQAYQX+laGjCj0vtb0f9EalXqWg78q77PPOOMM1Rtdu7cWeu9llZYWNjgshMm\nTFB33XWXuu+++1R0dLSKjo5WM2bMUG63WymlVEpKivrTn/6kpk2bpqKiotQNN9yglFLq6NGj6uab\nb/Y/c9lll6m9e/cG1P3cc8+pxMREFRYWpqZMmaL+9Kc/qZSUFP/9P/3pT2rQoEEBz8ybN08NHjxY\nmc1mlZCQoG6//XZ/OwD/l6+euXPnqrCwsIA6/vWvf6levXopk8mkevXqpd58882A+4B644031A03\n3KBCQ0NVjx491AcffBBQZtasWSo5OVmZzWaVmJiopkyZUuevY1P//JcvX96k9QWj9t7H9t4/pdp/\nHyv3r7w8Vy1fjkpP/0e9z61ff5bavPmiestlZ89Xy5ejli9HlZam+6+vWdNb7dhxiyotTfPfz8yc\n25gu1Kmun19ReZFiJv4ve7ldXTb/MsVM1PB/DQ8sPGWKUvHxSnk8Fdc++US5TSFqcyLK+CQq6YUk\nFfbnMNXpuU76/ePHlQKlXnyxyftVWVP8Hr3qo6sUM1HLFr2otxmUevBBVRaiqU8v6u4v5/u1mjRv\nkn5h9mx/+etuqvh1bEr19Q9YrxoQUwXTMOskoAuQpWmaS9M0F5ACPKdp2lFvmWzACMRVeTYBPTvX\nIc2fPx+Px8Pq1at54403ePPNN3n55Zf991988UX69+/P+vXr+ctf/kJJSQmTJk3CarWycuVKVq9e\nTZcuXbjgggsoKdF3Rv/Pf/7DE088waxZs9i4cSP9+vXjxRdfrLMdb7zxBnfddRfTpk1j69atfPXV\nVwwaNAiAX375BYC33nqLffv2+d9XtWjRIu69914eeOABtm/fzv33389vf/tbFi9eHFDu6aef5uqr\nr2bLli3cfPPN3HHHHRw+rE/UXbBgAbNnz+b1119n3759fPHFF4wePbpxv7hCiHbB5ToBgMlU+0a5\nPg2dM1dcvMP/2u0uqvRaH2a1WlOIjNRnADmdzT/fyqM8bM/djr3cztqMtQH3vtr3FT8d0YeWHe5K\ne+iVl8OyZXDuuYHDpTfdhOHlOQzLgd4n4JFzHuHYw8c4/IB3QUSGd8uVbsG/mcSAuAEAxCT1rrh4\nwQXs6hVJn0MF/kudbPrQ8x0jvJsfz5nDviFd8QBDvBFGTQsmgkEwDbO+DlQ99+Rb9Dl0b3nfb0Bf\nFHEh8CGAd1uSAcDPTdqaBx7QDw5uScOHQ6UgrKG6dOnCP/7xDzRNo3///uzdu5cXX3yRBx98EIAJ\nEybwyCOP+Mu/++67KKWYO3cumvcP7xtvvEFCQgJffPEFN910Ey+//DK33347d911FwCPP/44y5cv\nZ//+/bW245lnnuGBBx7wfy7AGWecAUB8vD7XIDo6msTERCIiImqsY/bs2UyZMoV7770XgL59+7Jh\nwwaee+45rrzySn+5KVOm8Ktf/cr/uXPmzOGHH34gJSWFw4cP06VLFy666CJMJhPJycmMGjXq1H5R\nhRDtitOpHywfEtKQYM6Gy5Vfb7nAYK5iqohvzhzAiBE/8tNPcZSVHTjVJp+S/Sf2M3HeRDLsGYzp\nNsa/8e21/a9l0e5FLNy1kFBTKAXlBf4TDgD4xz8gM1Pf+LeqkSMBPZhLiUrBZrJV3DvqzbEkJTVX\nl5rMM5Oe4ZLelzCi85iKiwMGkNfJxqA9FT/nWFss/eP6M3nIZHA64ehRTl58KVlpmaR4Y76soiz6\ndOrTwj2oX0vvMxeuadpwTdOGez872fs+WSmVq5TaXvkLPXDLVkrtAVBKFQDvAM9rmnaBpmkjgA+A\nrcDSluxLMBkzZow/KAMYO3YsGRkZFBYWAlQLZDZs2MChQ4eIiIggPDyc8PBwoqKiyM/P58AB/S+c\nXbt2MXbs2IDnqr6vLDc3l4yMDM4///zT6suuXbs455xzAq6NGzeOnTt3BlwbOnSo/3VISAjx8fHk\n5urLx2+88UbKysro0aMH06dP57///S/l5Y3fAFQI0fbt3asHKw3JzBmNYbjdxfWWKynZicmk/0e1\najBnNOqBj6Zp2Gy9KC1t3mBu3uZ5ZBdlkxCWwJqja9idt5vEsEQW3ryQyUMms/LwSkJNoUDF2aPk\n5MAzz8CVV8LFF1evtLeeyep9AjqHV5mW7svMtYFgzmQ0MTF1IlitFfvhJSeTFxdGfL4D3PrilcLy\nQgbFD9L/Pc3JAaUYMPQ80qIhxXuMa+XVr8GkpTNzo9Dnt/nM8n69B0xtYB2/R5839wlgA5YBt6nK\nS4maQiMyZMEqLCws4L3H42H48OF8/PHH1crGxtb/F11N9KH9pqHVsDKq6jVTlZ3GNU3D49EnInfv\n3p09e/awbNkyli5dykMPPcSsWbNYu3ZttV8LIUT753DkUFy8Haj91IfKjMbwgGHTmijlobT0ALGx\nF3HixDf+YM7hyMPjKcVs7uIva7P1orBw3Wn0oH4/HPmBUV1HYXfY/fuixYXqM5LGJY9j/rb5/mCu\nzFWmP/TNN2C3w6xZNVfaqRP5VuhTVzDXpUv154LZ7t1w5AgYDJyICyXEo/TArWtXCssLibR4V7xm\n6cOp4al9ORwFY7yJyGAN5lp6n7kVSimthq+ptZRPVUrNrnKtTCn1O6VUJ6VUqFLqSqVvT9JhrV27\nNiCYWrNmDV27diUysubdzUeOHMn+/fuJi4ujd+/eAV++YG7AgAGsWbMm4Lmq7ytLTEwkKSmJZcuW\n1VrGZDLhdtcdcw8YMIAffwxcafbjjz8ycODAOp+rymq1cvnll/PSSy/xyy+/sGPHDn76qfatCIQQ\n7dehQ08CoGkhNZ7HWpXRGIbHU3dmzuMpBTxYLPqcMV/wV1KyG4DQ0AH+slZrL8rKDuOp4RzQppJX\nkkdSZJJ/Ptyg+EF8dP1HAIxPHq+3zanPiY6xerNT+/aB0ahvFFwTTSM93kLvE5AYnhh47+hRSEiA\nRu6O0GoSEsA7WlUY6/3PfXY2TreTUldpRTCX6Q3aunQhKwISvb8dJJgTzSYzM5MHHniAPXv28Omn\nn/L888/z+9//vtbykydPJjExkauvvpqVK1dy6NAhVq1axUMPPcS+ffsAuP/++3nvvff8Cxb++te/\nsnbt2lrrBH1e3csvv8xLL73E3r172bx5My+88IL/fmpqKsuWLSMnJ4f8/Jrnozz88MN88MEHvPba\na+zbt49XXnmF+fPnB8z5q8+8efN4++232bZtG4cOHWLu3LmYTCb69Am+eQ5CiOaVnf0+WVlvkZz8\nRyZMcPqHP+vSkMyc260HRiaTfkqAy6Vn5uz29QCEhvbzl7XZegJuysuPNKYLDZJfmk+MNcYfzP3n\nxv8wJHEIAAPiBxBrqxh1+Xry1/qLffv0c1XrOFO115kXcU5ZPNYQa+CNjIw2sfihLo4wb5/sduwO\n/edXNTNHly4ct0GYE7qb4yWYE81n8uTJuN1uzjrrLH79618zffr0OoO50NBQVq1aRc+ePbnxxhvp\n378/t99+O/n5+cR45xPcfPPNzJw5k8cff5wRI0awbdu2gIUNNfnNb37Da6+9xltvvcXgwYO55JJL\n2LGjYoLwCy+8wPLlyxkwYAAjRoyosY5rrrmGV155hZdeeomBAwcyZ84cXn/99YDFD/WJjo7mnXfe\nYfz48QwePJgFCxawcOFCevTo0eA6hBDtQ1bW24SFDaZHj6cb/IyemSujptk7vuyax6MHc2aznrHy\nDbMePfoiERFnYbVW/H3jO22iOefN5ZfpwdzMCTMBSI1O9d8zaAbO6a7PRb6639UVm/7u2wf1/Cc3\nbNBwwrOOg8MReCMjo03Ml6uLM9wb2BcWUliuzzEPCOY0DRITOa6PTjPSnBKwyXAwCabVrKKRQkJC\nePXVV3nVuxFiZWlpaTU+k5iYyNy5c+us97HHHuOxxx4LuDZz5syA15XfA0yfPp3p06fXWN+VV17J\nlVdeid1u969mnTp1KlOnTg0od/fdd3P33XfX2q6a5udV7uc111zDNddcU+vzQoiOo7w8k8jI0ZzK\n8d0Ggz785naXEBJSsfI+I+Of7N//e4YPX0FIiL4BcMUCiCKcznzKy9NJSrovYJ6v1aoHT1u3XsS4\ncYUBddZHKVXjPOLKylxllLnKiLHFMG3ENKaNmFatzPjk8Szeu5gwc5ivYti7F8aPr7sBvXuDxwNp\nadBXP3ie8nL9fZXFam2NM0wP5uzHMrCXpwIEDrMmJEBICHneYK4PsXxuT2v5hjaAZOaEEEK0UwqH\nIzNgMUJDGI36SQ5Vh1pzcj5AqXIyMl7xZ+aMxjAMhjDcbjslJfoplGFhAwKes1i6+l9nZPyjwe1w\nu8tYudJAenrde3zml+rTVvxz4WowPkUP2sJN3lMqsrOhuLjezJz/vncKDgDz5kFhIVx7bd3PBjlX\nmB6lPfnZAzVn5ryLO+65RJ9zmeqMkGFWIYQQomUV4/GUBgRTDWE0+jJzgYsglNJXzB87toDycn15\no8EQSkhIREAwFxraP+A5TTOQmHgbAOXlDR+my8v7HwBHj75UZ7n8Mj2Yi7bWvlJ3ZJeRRFujK1al\n+oKz+oI57/Yk7Nmjf3c64W9/g7POggsuqPvZIOfyZuYsJeU1B3Nd9d835426EYBuThtFjiLs5cFx\n9GdlMszaxrXncxWFEOL06KcuNFVmzu0uwmrtSVnZQTIz3/KWDcVojMDlslNcvAtNs2C1plarc8CA\n98jPX4JSjmr3alNWlgaAxdK9znK+DYJjbLVn5sxGM5vu2uTfrqTBwVx8vL7QwXdqz5df6kOsc+YE\nnhjRFoXacGkQWY4/mIuweIfAMzPBN7e7sx4AJxUBofqK1n6WfjVU2HokMyeEEKKd0vdC8y1AaChf\nZq7q9iQeTzFRUeMIDz+DEye+BPTMnNEYgdtdREnJLkJD+9U6P6+hx4T5uN36sQNO5/E6yzVkmBX0\nRRHhZu8w6759+irW5Pq3amHsWP3IryNH4IMPwGaDSy+t/7kgZzKaKbQEBnORlkhwuSA3t2IPvbg4\niIggIVvPyPn28QsmEsxV0pQb34q2Q37uQrRX+lYgNtupZVHqyswZjeF07nx7pbKh3q1M7N5gLnC+\nXGWaZj6lYM7l0gOM8vKjdf495RtmrTEz53LB44/DxIkwZgx88YV+fe9e6NkTQhowQPfww1BaCikp\nsHChHtzVsZ1JW2Eymii0QFRZlWAuN1df9OEdZkXToHdvwo/oB7T6N10OIhLMeZlMJkpLS1u7GaIV\nlJaWVjtRQgjRHqRjMiViMtV/6kNltc2Z8wVzYWGD/NcMBhtGYwROZy5lZWnVFj9UZjBYUOpUgjk9\nM+fxlPgXXNSkzszca6/BX/4CRUWwaxe8+65+fcMG/TzwhjjzTPj2Wxg2TK+rnp0Q2gqP8pATDp2L\nKoK5cHN4wB5zfsOGEfPjLzy1Akqdtf8sWosEc14JCQlkZGRQUlIimZoOQilFSUkJGRkZJCQktHZz\nhBBNLrfG+Wv1qcjMVQRzSrnxeMowGsMC9pAzGvUFEPrJD6ra4ofK9GHWhs+Zc7sL/a/rGmqtcQFE\naSmcOAFPPw0XXqjPebviCli0CBYs0IdMzzyzwW3h7LNh82Z47LGGDc22AS6Pi8wI6GoHu8NOmCkM\ng2aoOZibPZvCKy9i1gqIXlH7aUitRRZAePmOvsrMzMTpdLZqW8rKyrBarfUXbKOCqX8mk4nExMRa\njz4TQrRleVgsZ5zyUxX7zFUMs/oCO6Mx3H+El++90Vixb1zdw6ynNmfOl5kDcDrzsFprDqLyS/MJ\nM4VhMnpHGD74AKZP14cKPR54/nl9qPCGG+DDD/XvXbvC9dc3uC3tkdvjJiMCJqTpR5359+DzHeXV\ntdIq6E6dOPHaC5gWf038il+g9q1QW4UEc5VERkYGxT/qK1asqPWEhPagvfdPCBEsjp/ytiRQc2bO\nF9gZjeEYDCa6dXuIiIgRGAwWf3kwYLP1rbVeg8F8ysOsISExuFz59Wbm/PPlnE546CF9g9/zz9dX\nqw4bpt+79lp9ztxnn+nDpXFxDW5Le+RWbjIjILYMnEWFhJq8uwP7MnOJgefRWsOj2dQFUg8cbeGW\n1k+COSGEEO2OfnZqEWbzqR85ZTSGeuuonJmrCOYAeveeXam8npmzWlMwGmsfdTAYLDidxbXer8rt\nLsBq7UFRUe3B3LHiY3yy4xOGJg7VL6xaBceOwZtvQk0n4Vx+uf4lcHvcHPTGwLFpOYRGVgrm4uLA\nbA4obw2xkhUO/fNOtnBL6ydz5oQQQrQ7dvsGAGy23qf8rKYZMBhsAVuTVARzYdXK+4K5+vazO5Vh\nVperiLKyI0RE6PPaNmdvZunBpdXKvbXxLcpcZbxw0Qv6hR9/1IdU2/iGvi3Brdz86B257r0jsyIz\nl5kZOMTqZTPZyA6HsLyCavdamwRzQggh2qySkv01LirIzf0EsBAbe0mj6jUaw0lPn82uXVOBwDlz\nNZWt7V5lpzLMarevBzx06nQZISEx/GvbUu764i4+3PYhSw4s0duk3Lyx4Q3O73E+45LH6Q/u3asv\nUAivuy1CXwCREQUHYmDAjlw9mFMKtm6FXtX3JrQYLWRFgLWoDMqCa3sSCeaEEEK0SWVl6axb14dD\nh570Xztx4js2bhxLbu58YAwhIY0LanwZuJyc94Dqw6yBZcMDnqnNqWwabLevBSAq6hzGjTtBYvQo\nDuYfZPLCyVz074sAWHdiHUcKjvCbUb+peHDvXugXXKcTBCuXxwXAqhQYsreAMKMNtm2Dw4fhssuq\nldc0jbxI74bQWQ0/lq0lSDAnhBCiTTp5cjkA6el/Z8eOmzh+/Cu2br2YwsI1uFwngUmNrrtq0OYL\n5nwrXStTyuV9pu5gTh9mrX9rkhMnviMtbSY2Wx9Mpk4AFSc3eB0rPsbG/I3YQmxc1e8q/WJhIWzf\nDoMH1/sZQs9sAqxMgZhiN31zXLB4sX7ziitqfGZbrP7M3u8/bZE2NpQEc0IIIdqk/Pxl/tfHjv2X\nbduqTuwf0+i6KwdtHo+zzsycb25d/cOstW8aXFKyzz+Uu3XrxXg8ZURGnuW/H2GOCCi/6vAqSt2l\nxNhiKrYkWbhQH/674YZ6eidAHzYFPTMHMHxvIXz+OYwe7T+PtaoNXaHMCIXLvmqpZjaIBHNCCCHa\nHKVUQDDXq9dshgz5gh49ngXAbO4MWBpdv8FQcSqMy3WizmAuLu56bLZ+dOv2YD116sd52e2bOHbs\nf5X64mbdur7s2HFTQPnKGxD7D4D3+nzv59hd9sCM3fz5+hFdYxofxHYkf7/w78wYO4O8xHBKQ6Df\n/pOwbh1ceWWtzzhCYF0SxGzY3oItrZ9sTSKEEKLNKS3di8ORQWrqLKzWnsTHX4fRGEqnTpcTGTmG\n0NCBrF695zQ+oSLXoZS7zgUQFktnzjprd701+lazbtgwEoCJE/XThpzOEwCcPPk9+/fP8JePja2Y\nt5WSdpLrd8CCQTC221je3/I+ACO76HWRlQXff6+fw6ppp9LRDivWFsvzFz1PXmkex0Ln0W9Htn6j\nnmD4x2R4+Oc8KCmB0NAWaGn9JDMnhBCizTl5cgUACQm30rnzr/x7wwHExJyPxVL3NiH10bTKwZxv\nmNWAwXA62T4LSlWfM+dw6Ae4h4REc/SovsVIcvIfiYio2Fx91PtLeXMxWJwwKL7ibFh/Zu6nn/TT\nHq66qtHt66iuH3A9x8IgNteuX6hhJavPBT0v4Jve8N4wOJ6T1jINbAAJ5oQQQrQ55eVH0U9c6NlM\nn1A5mHPhdhdhNIajnUbWq7ZA0OnMBcBojKq1bNZNlxJbBhce1IcHffzBXLY3q9ROzk1tSRf2vJDS\nGO+vY0gIdO9ea9klU5bw9uw9mOe+h7lr7eVamgRzQggh2hyn8wQhIdEBGbSmVRG0VQ7mTqtGLfBE\nAaX0YVaHQw/mQkIqjpN0u+0BZd0jhgPQ+wTE2GKYMnQKUCWYMxqhU6fTamNHZAmxMG6k97SMlBQ9\noKtD3059uW3YbdXmMbYmCeaEEEK0KZmZb5GZ+fppB1d1qRwkejxOHI4MTKb406qzarYtPf3veDzl\n/sxcSEhFZi429tKAsqEJSRSa4Ry3fjxZ1wj9hAL/qQXZ2ZCQoAd04tT5zmFNTW3VZjSWLIAQQgjR\nZhQWrmfv3jsBcDiym/GTAodZ7fZNjT5Nwl9jlWDu4ME/EBo60LsnHhgMVmy2voSHjyAm5ryAssO7\njCA/pRvXWPUzWPt26gtApj1TL5CdDV1Ob55ghzZcz3wG28kODSWZOSGEEG2C03mCnTtv9L+vaTFB\nU6mcmXM4MnA6cwIWJDRGTXPmysuP+odUlXLh8ZTUuPmwpmnE9h9ByJGjAIzppq+43J3nXUV79KgE\nc6dj9Gj9exs9PUOCOSGEEEFJKTfl5Zn+9wcPPkp5eQZDhrTEhq0V/zwWF+8CwGY7vX/oq86ZAygv\nz8DlKgTA4ynH7S4JWJkbIDUV0tJAKfrH9Wd83Hjevepd/V5aGvTocVrt69D69oUlS2DOnNZuSaNI\nMCeEECIopae/xOrVSZSU7AfAbt9ITMz5/uHOsLAhzfbZqakV572WlOjZL5ut92nVWVNmzuHI8Gfm\nPJ5yPJ5SDAZbbY3Sj+w6eRKDZuDpQU9zfs/zIT8fCgokmDtdF1wA4c03D7M5STAnhBAiKBUVbQQg\nK+stlFKUlx/BYklB0zRGj97N8OErm+2zIyPPYtgw/exXPZgzYrWmnFadtQ2zVmTmSr3BXB2ZOYCD\nBwOv+9630cn74vTJAgghhBBByXfIfHr633E4snA687Ba9X3UQkObf26T70ivkpLdWK3JAUd8NUZt\nw6y+VbIuVwEARmMtmbkzzwSDAS67DHJzGTFokH781Lp1+n3fJH7R4UhmTgghRFByOI5htfYkMfF2\ncnI+AMBiablNcTVNz3e4XMdPe4gVasvMVQyzulz53nK1ZOa6d4ff/94/ST9qxw5480344Qfo2lWG\nWTswCeaEEEIEJaczD7M5kZiYC/zXmiKoaihfMKd/bu1HPDVUTcGc213oX+ThC+ZqXQABMHs2rFoF\nHg8l3bvD8uXw448wbpycydqBSTAnhBAi6LjdJZw8uQyTKZ7Q0D7+6y0xvOoTGMydfhCpabUd55UT\n8L5BCzs0jcKBA+HzzyE9HcaPP+32ibZL5swJIYQIOsePLwbAbO5MaOgA/3WTKabF2qBpFXPkrNam\nyMzpc+bM5i44HFk1lomLu4bIyNENqu/w5Ml0PvNMfQXmlCmn3T7RdkkwJ4QQIug4nXkA9OjxNCEh\nkYwatcV/hmlLaerMnG+Y1WJJqiGY04/h6tHjrw2ur7R7dwniBCDBnBBCiCDkO+IqJCQagPDwoS3e\nhsBgrmcT1KcHcyEhsZx99jHc7gLWrtWDxJSUJ4iLu4awsP6n/Tmi45FgTgghRNBxuU5iMNhqXDTQ\nUioHc3UuSmggX19MpljM5jggjpCQGFyufKzWZCIiZGsR0TiyAEIIIUTQcblO+rNyraXynLmm4Jsz\nFxIS679msSQBYDRGNOlniY5FgjkhhBBBx+UqCIJgrmkHryqGWSsWcVgs3QAJ5sTpkWBOCCFE0HA4\nctm+/XoKC9e1u2DOaLSSmvoMiYm3+K+ZzXpmLiQkskk/S3QsMmdOCCFEUCgsXM/GjWf634eFDW7F\n1jT9MCtAauoTAe9lmFU0BQnmhBBCBIUDB2YA0LnzVDp1uqpJVpCejqbOzNVE3/JE85/PKkRjSDAn\nhBAiKPhWe/bu/QohIeGt3JqWCeYSEm4hLGwQFkvnZv8s0X7JnDkhhBBBwek8Rmzs5UERyAFomrHZ\nP8NgCCEiYmSzf45o3ySYE0IIERQcjhzM5sTWboafJgfXizZCgjkhhBAtQinFgQMPs2KFxs6dt1a5\n58HpzA2qYE6ItqJFgzlN087VNO1zTdMyNE1TmqZNrXL/GU3TdmuaVqxpWr6macs0TTu7ShmLpmmv\naJqW5y33uaZp3VqyH0IIIU7dgQMPk54+G4Dc3I/weJwAbNlyET//3BmlXISGDmzNJlYzYMC/GT16\nd2s3Q4g6tXRmLhzYDtwPlNZwfw9wDzAEGAccAr7RNK3yf9VeBq4HbgHGA5HAF1pLTG4QQgjRYEq5\nOXLkeZzOfFwuO0ePvkRCwv/z3z9+/EuOHVtAfv4SnM5jxMRcSGLirXXU2PISEycTGtqvtZshRJ1a\ndDWrUuor4CsATdPm1XD/35Xfa5r2IDAdGA58q2lalPf9NKXUEm+ZKcBh4ALg2+ZsvxBCiLq53SUU\nF+8gMvJM7PZNHDz4CC7XCe8RVh46d76Dbt0eYOPGMezYcW3As/37v4+myewfIU5V0G5NommaGbgT\nKAQ2ey+fAZiA73zllFLpmqbtAs5GgjkhhGhV2dlz2bfvXs44YyNO5zEAjhz5GwChoYOIijoHg8FC\naurTWCzd2LPnDgD69PmnbM8hRCNpSqnW+WBNKwLuVUrNq3L9CuBjIBTIAq5VSq3z3rsVeB8wqUoN\n1zTte2CfUuquGj7nTuE3YPIAACAASURBVPSgkMTExDM+/vjj5ulQEyoqKiI8PDiW5jeH9t4/kD62\nB+29f9BcfXwN+BQYBMQDKyrd+wx9Zkxl1wH5wFPApCZtSXv/Gbb3/kH772N9/Zs0adIGpdSo+uoJ\nxszccvRh1Tjg18B/NE0bq5TKquMZDagxKlVKvQm8CTBq1Cg1ceLEpm1tM1ixYgVtoZ2N1d77B9LH\n9qC99w+ap4/bt79CXh7ADv81g8FGePgIRo68qlr5tWvjKC3NZ+jQccTGNm1b2vvPsL33D9p/H5uq\nf0E3OUEpVayU2q+UWqOUmg44gf/z3s4GjOiBXmUJQE4LNlMIIUQVSins9l8IDR0QcP3ss3MZNmxZ\njc/4ziQ1Gttv9kWI5hZ0wVwNDIDF+3oDenB3oe+md1uSAcDPLd80IYQQPpmZr1Nenk5s7GX07Pl3\n//WQkHCMRmuNz8TH3wiA2Szz5YRorBYdZtU0LRzo7X1rAJI1TRsOnABOAo8Ai9HnysWjb1PSDfgP\ngFKqQNO0d4DnNU3LBY4DLwJbgaUt2BUhhBBAbu6nZGW9hdmcQG7uf4iIGE3Pnn+juHh7g55PTn6U\nxMRfYbXKdqFCNFZLz5kbhT4nzmeW9+s94LfoM2bvADqhB2q/AOcqpbZWeub3gAv4BLABy4DblFLu\nZm+9EEII8vNXsHv3VAYO/IidO2/0X+/S5U569HgagyGE0ND+xMZeQkrKU3XWpWmaBHJCnKaW3mdu\nBfpihdpcW8c9Xx1lwO+8X0IIIVpYYeHPlJcfZvNmffVpWNhQBg/+HzZbD38Zo9HK0KFft1YThehQ\n2sKcOSGEEEGkrOwwRmMkJlMcYGD48O8DAjkhRMsKxq1JhBBCBLGyssPYbH0YPHghpaX7MJk6tXaT\nhOjQJJgTQgjRYG53McXFW4iKGofVmozVmtzaTRKiw5NhViGEEA2Wnf0eDkc2Xbve09pNEUJ4STAn\nhBCiwXJyPiQ0dBDR0RNauylCCC8J5oQQQjRIaWkahYU/kZg4GU2ra2MCIURLkmBOCCFEg+TmfgRA\nQsItrdwSIURlEsyJ/9/efcfrPd//H3+8sk/2PomRQRoSkSCoGBF7FbVqlFJao4rSmrVVVWm/aItQ\nq2rV+KlaVSRKCIIiYmWQRCJ7nQwZ5/374/X5uK5zck7Ouubnet5vt+v22Z/r/cq5cp3XeX/eQ0Sk\nXubOfZCOHXehrKxfvosiImmUzImISJ1WrPiM5csnUl6uWjmRQqNkTkRE6rR06VsAdOqkjg8ihUbJ\nnIiI1GnZsrdp1qyMtm23zHdRRKQaJXMiIrJBlZVrmTfvMTp33pNmzTTWvEihUTInIiI1Wr16PgCr\nVk1j9epZ9OhxeJ5LJCI1UTInIiLr+eqr23n99R4sX/4Jq1Z9CUCbNpvluVQiUhMlcyIiUsWqVTP5\n/PMzAHj77UEsXfo6AG3a9M1nsUSkFkrmRETkW+vWreSjjw6jefP23+774osrAGjdeuN8FUtENkAt\nWUVE5FuTJ1/KsmUTGDLkScrKtmD16llMm3Yp7doNoVmzVvkunojUQMmciIgAcOWVq9lxx3vp0+cY\nunc/FIB27bakS5fX81wyEdkQPWYVERFmzYK77vqCtWvb07XrD/NdHBFpANXMiYgI778PM2cOpFOn\nafTuHfJdHBFpACVzIiLCBx/4cujQZpjltywi0jB6zCoiInzwAfTpA1265LskItJQSuZERIrI8uWw\nYkXm7/vBBzB0aObvKyLZp2RORKSIHHoodOsGS5c2/h7r1sH//R8sXeotbb75Bj75RMmcSLFSMici\nUuAWLIDTTvNOCi+9BKtWwd//DkuWwHvv+faxx8J559Xvfi+84Ofecst3APj4Y1i7VsmcSLFSBwgR\nkQJ3ww1wxx3w4IOpfXfeCWPGwGOPeU3dggXQsaOf27z5hu/38su+/PDDToSQ6vwwbFh2yi8i2aVk\nTkSkwH3+uS9HjYKuXWHQILj4Yvjf/3z/ggWw556epI0bByNHbvh+kyf7cu7cNnzwgSdzbdrAgAFZ\nC0FEskjJnIhIgZsxA/bZB/71L98OAbbbDsaOheuu830PPghDhsCtt9adzM2eDdtsAx98ENhmGx+H\nZNNNoYV+I4gUJbWZExEpcNOn+7AhMTPYd1/47W/hiy/g1VehvBwGDvRauhUrvE3cccfVfL+vv4at\nt4Ytt0z1othrr+zGICLZo2RORKRAXXwxDB8Oc+bU/gi0b1/YdVdfb9cOKirgqqu8t+pDD61/fghe\nM9e7N+y88wIA/vxnuPnmLAUhIlmnSnURkQIUAtxyC/TqBT/9KZx5Zt3XtG/vtW7jxqX2VVZCs7Q/\n2xcuhNWrPZnbd9/pHHnkZuy7L5r1QaSIKZkTESlAs2b549Jf/hJ+9rP6XdO+vdfMzZuX2rdypdfY\nxb780pd9+3qvVz1eFSl+eswqIpIjb74JJ5/stWV1+fRTXw4cWP/7t2sH06Z57Vx83fLl8MYb8M47\nvj1tmi/796//fUWksCmZExHJkdGj4Z57vA1cXZ58Elq1gm23rf/927dPre+2my+XL4edd4btt/fH\nq2PH+v5+/ep/XxEpbErmRESybPlyX77yii9nz/bl6tVw990119S9/LIPR9KtW/3fJ/1x6s47+zJ9\nHtfWrb2zw9Ch0Llz/e8rIoVNyZyISBb9739eY3bzzTB1qu+bOtVndGjdGk45BR54YP3rvv7a27U1\nRFwzt/HGPlQJpBLJ2COPwGuvNey+IlLY1AFCRCSLJkzw5S9+kdp37LE+F2os7rAwfrwP/NuqlY8X\n16tXw96rrMyXW26ZqqVbtix1fNQo+MEPGnZPESl8qpkTEcmQ11/3x6LXXgvz53ut3IwZ65+3dm1q\nbLh4+7zzYMQIuP12mDvX9zc0mVu0yJcHHQRt2/r6rFm+vPRSePrpht1PRIqDauZERDLkmWd8HLdL\nL/UX+Fyqsb32gu7d/VHn2WenHnf+5jepGrQHH4R77/X1hiZzZ5zh48WdeWZqPteZM305YEDVNnUi\nkhxK5kREMmTCBJ/zdPFin2YLYKutYP/94cgjvdNBhw4+Q8MWW6SuW7YMjjoKHn0U3nvPz9l2W++B\n2hDdusGvf+3rcc3c5Mm+7NKlSaGJSAFTMicikiFffOHJ3IgRcO65/oh1k03WPy89kYvde68nc+DT\ncB10UNPKstFGnjzefbe3pRs2rGn3E5HCpTZzIiIZsnCh146dc473Iq0pkavJuHGpmjTwqbaaqnVr\nuPBCGDnSBwxuaM9YESkeqpkTEcmAykpP5rp29XZr6clZbeKersOHV92fiWQO4KKL/CUiyVavZM7M\nyoALgCOAzYAATAUeBf4QQliZtRKKiBSBJUs8oWvIIL/Vk7hYz56ZKZOIlIY6kzkzawG8DGwHPA88\nAxgwGLgcOMDMdg8hrK39LiIiybN2LRx+uCdwJ5zg+xqSzFV32WXw179C8+aZKZ+IlIb61MydCgwA\ntgshfJR+wMyGAGOic27NfPFERArX5Mnwr3/5+n33+TJ9KJKGuvpqf4mINER9OkAcCVxbPZEDCCFM\nBK6LzqmTmY00s6fM7CszC2Z2UtqxlmZ2vZl9YGbLzWy2mT1oZn2q3aO1mf3JzOZH5z1lZvVsZiwi\nkjmTJvnyiSfgmGOgZUuffUFEJJfqk8xthT9mrc2LwJB6vl97YCJwDlC9nV1b/FHutdHyUGBT4Pno\nUW/sJrzt3rHAbkBH4Gkz04MJEcmqykoIIbUdJ3P77OOD/X7zjQ/OKyKSS/VJ5roA8zZwfB7QuT5v\nFkJ4NoRwSQjhMaCy2rElIYR9QgiPhBA+DSG8BZwGDIpemFkn4BTg/BDCf0II7wInAEOBvetTBhGR\n+vjmm2bceqtPkXXWWT5V109+At/5jvdaBZ9LdcstUxPcm+WvvCJSuuqTzDUHNtS5oTI6Jxs6Rsto\nxkGGAy2BF+ITQggzgI+BnbNUBhEpQWPH9uDMMz15+/OfYffd4Z57YMoUT+zWrPHx4XbZJd8lFZFS\nV58OEAb83cy+qeV46wyWJ/WmZq2APwD/CiFEswvSC1gHzK92+pzoWE33ORXvoEF5eTljx47NRnEz\nqqKioijK2VhJjw8UYzFavrw5v//9lhx22EyaNYPx43vQrFmgXbsVLFjQjrVpf9JOmPAR770XWLx4\nCJtv/iFjxy7IX8GbIGk/w+oUX/FLeoyZiq8+ydx99Tjnb00tSLqojdzf8ce3h9TnEnzsu/WEEO4A\n7gDYfvvtw6hRozJUyuwZO3YsxVDOxkp6fKAYM+nGG+H882HlSmjTJnvvs9VW3gZu3LgerFvn+/r1\ng08+acc118C116bO3WyzrXjkEZ8y6/zzt6ZFkQ6/nvTPqeIrfkmPMVPx1fkVFEL4cZPfpQGiRO4h\nYGtgVAgh/U/er/FHut2p2o6vJ/DfnBVSRHLmppt8OXs29O+fnfdYtizVmSFO5MCHGWndGn7xC7ju\nOrjmGp/I/tNP4fnn4eKLKdpETkSSo9Fzs5pZHzMbbJa5Jr9m1hJ4BO/QsEcI4etqp7wDrAH2Sbtm\nE7yDxOuZKoeIFI6OUcvZr77K3nvMjBpy7LYbjBjhid13v7uAO+7w/d27exu5M8/07Vtv9Z6tJ5+c\nvTKJiNRXncmcmR1tZmdU23cbMA34EJhoZhvX583MrL2ZbWNm20Tv3Sfa7hPVyD0K7IQPOxLMrFf0\nKgPv8QrcBdxgZnub2bbA/cAH+BApIpIwcTI3fvz6x558Er78sunvESdzv/mNd24YNAh+97sPq0y3\n1axZar7VxYu9Q8RmmzX9vUVEmqo+NXNnkTaMiJntjQ8ZcjlwVHSPy+r5ftsD70WvMuCqaP1qYBN8\nbLmN8Bq42Wmvo9PucS7wBF6DNw6oAA4OIaQ9HBGRpIiTufPPTyVd4DVjhx3m7dqWL2/ae8yY4ctN\nN93weS1bph6rDqnv6JoiIllWn9YeWwBvpm0fCrwQQrgWwMxWAX+uz5uFEMbinRVqU+cj2xDCKjzB\nPKs+7ykixS19ntJrroHRo3198eLU/iOP9Gm1Gtt+bdIkbxu3ST3mkokblvTu3bj3EhHJtPrUzLUH\nFqZt70zVGSE+opZhQUREmmLWLO9osOee8POfw113+XyoAPOjAYp2393POf/8xr/Pu+/CsGFe81aX\nNWt82UvfeiJSIOqTzM3Ep/TCzDrivUzHpR3vhj/qFBHJiLffhjlzPIkDbxd30UXe0/Txx+H222Ho\nUD920UVwyCHefi7d6NE+wG9dQvBkbrvtGlZG1cyJSKGoz0OJR4FbzOw6YH+8DVt6U+TtgU+yUDYR\nKUHPPw8HHODr8SPWqVNh4419NoaLLqp6fvfufuz1tP7sX38Np5/u7e2WLNnw+02d6uc0NJnr06dh\n54uIZEt9auauAd7AZ2PYGji+WmeDY4FnslA2ESlB11+fWr/mGl/G48vFU2elt6Pr1s3nRl22LLXv\n/fd9uXQpPPGE177VZMYMGDDA1xuazA0a1LDzRUSypT6DBq8EfrSB43tktEQiUrLGj4exY+Gyy3zA\n3lNPhZ13Tg0BsssucO+9PhPEb38L55wD5eXQoQN88423Z2vZEt6MumwNHAhHHAFdusAf/gAvvOD7\nrrrKj99zT+q969s7dccdvSavebZmpBYRaaA6kzkzW0bNU2UtAT4Fbggh/DvTBROR0nP99Z54XXCB\n17aBd3CIHXSQJ3cXXujt5M4+2/d36ODLigq//qGHYORIeOQRb9u2aFHVAX5PO82n4urcObWvdT1n\nmR4/vvaaPhGRfKhPm7mf17K/MzAceMrMjgwh/CtzxRKRUlNeDnPnwuWXpxK56nr3hnHj1t8fn19R\n4euffAJHH+09Tn/3u1Q7u7/9DX70I28nt24dvPqq749neqgPs9TwJCIihaA+j1nv29BxM3sPuARQ\nMicijbJ0qSdyAGc1YgTJuGYuvd3cRhv5co+oIciLL6YGBZ4yBfbe2x/Ndu0KP/1p48otIlIIMjFF\n9DP4bBAiIo0ybZov773Xe6c2VJzMzZ3rtXOQSuZ23BFWrICyMli92mvVxo/3RA5g4cL17yciUkwy\nkcy1AVZl4D4iUqK++MKXgwc37vr4Mesee6SSuPRx4MrKfNmqlT/O/fvfU8e+//3GvaeISKHIRDL3\nE+B/GbiPiJSozz/3ZWMnrk+fjaFZM+/R2rdvzedutJEPErz55vDxx2r/JiLFrz69WW+p5VAnYDtg\nM2BkJgslIqXlf//zgX+7dWvc9QMH+qPafv1g5UqYObP2x7Ubb+zJ3BFH1G/6LhGRQlefmrmta9m/\nFHgOuC2EMC1zRRKRUhICvPUWbLtt0+7Tr58vy8p8pojaxI9hDz+8ae8nIlIo6tObVYMCi0jWjB/v\nj1nPOy8373fggd5RYocdcvN+IiLZlok2cyIijfbnP/scqscfn5v3O+QQf4mIJEV95mYVEcmKOXPg\n0UfhpJNqHyhYREQ2TMmciOTNmDE+n+qPap39WURE6qJkTkTyZuJEn7C+vpPci4jI+pTMiUjeTJoE\nAwbUf5J7ERFZn5I5EcmbadN88F4REWk8JXMikjdffQWbbprvUoiIFDclcyKSVZde6nOmxr7+Gp5/\nHlatgnnzfEYGERFpPCVzIpJV114LY8fCuHG+ffPNcMABcPnlvr3JJnkrmohIIiiZE5GsiqfP+uMf\nfTlzpi9vuMGX22yT+zKJiCSJZoAQkayqqPDls8/C8uX+mHXYMNhnH28v19Q5WUVESp2SORHJmlWr\nYOlS2GsveOklbys3ezZ85zupmjkREWkaPWYVkayZN8+XRxwB3brBAw/Ap59Cr175LZeISJIomROR\nrJkzx5cbbQT77gv/7//B2rWwww75LZeISJIomRORRnvsMe+Z+uWX0KULlJfD5Mmp43Pn+rK8HM49\nF777Xbj/fvjxj/NTXhGRJFKbORFplGee6cWNN/r6HnvA4sW+/v3vwy23eOeGuGauZ0/YbDMYPz4/\nZRURSTLVzIlIFZMmwYsv+vpbb8Guu3onBoAQUueNH9/t2/Vp01L7P/rIOzwMHAgXX+z7ysuzXGgR\nkRKmZE5EqthqKx82BOCSS3yw32ef9fZu5eXwyit+rGPHtYA/Nu3UCc45x3uqpk/PNWcOtG0L7drl\nOAgRkRKix6wiAnj7trffTm2HAGVlvn7ssWDm+95/H3bfHRY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P0cMP931lZd5+cNNN4d//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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "est_df = pd.DataFrame({'est': est.reshape(-1), \n",
    "                       'date': df[num_train+num_cv:]['date']})\n",
    "\n",
    "ax = train.plot(x='date', y='adj_close', style='b-', grid=True)\n",
    "ax = cv.plot(x='date', y='adj_close', style='y-', grid=True, ax=ax)\n",
    "ax = test.plot(x='date', y='adj_close', style='g-', grid=True, ax=ax)\n",
    "ax = est_df.plot(x='date', y='est', style='r-', grid=True, ax=ax)\n",
    "ax.legend(['train', 'dev', 'test', 'predictions'])\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Zoom in to test set')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xoy8X1C2JUqY4tOS1wHh/LtVk0/YNMAJoCAwCPDBa7zYDz2it076V/zrGe3k/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RkzERHpVb6793fZadBOnPfIeaxvXA9A7Zb9GfEFDEzkaIKdNi1Mt/W1r+WppO2XbJ6u6VvD2MFj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\nnoiIiEgJUpInIiIiUoL+P8rNFSL4CDIAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time, for test set only\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "ax = train.plot(x='date', y='adj_close', style='b-', grid=True)\n",
    "ax = cv.plot(x='date', y='adj_close', style='y-', grid=True, ax=ax)\n",
    "ax = test.plot(x='date', y='adj_close', style='g-', grid=True, ax=ax)\n",
    "ax = est_df.plot(x='date', y='est', style='r-', grid=True, ax=ax)\n",
    "ax.legend(['train', 'dev', 'test', 'predictions'])\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")\n",
    "ax.set_xlim([date(2018, 4, 1), date(2018, 11, 30)])\n",
    "ax.set_ylim([130, 155])\n",
    "ax.set_title(\"Zoom in to test set\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Findings\n",
    "* On the test set, the RMSE is 2.988 and MAPE is 1.886% using N_opt=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
